solving problems on functions: role of the graphing calculator
SOLVING PROBLEMS ON FUNCTIONS:
ROLE OF THE GRAPHING CALCULATOR
To study the roles that the graphing calculator plays in solving problems
about functions, a small quasi-experimental study was conducted with
four pairs of undergraduate students solving problems with and without
the graphing calculator. The analysis of the protocols of the sessions did
not reveal major differences that could be attributed to the presence or
absence of the tool but indicated differences in strategies used with each
problem that could be explained in terms of the nature of the knowledge
at stake and to students’ availability of that knowledge. The study suggests a model for conducting research that looks for explaining the effects of technology in learning and instruction.
Keywords: Functions; Graphing calculators; Problem solving
Resolución de Problemas sobre Funciones: Papel de la Calculadora Gráfica
Con el fin de analizar el papel que la calculadora gráfica juega en la resolución de problemas sobre funciones, se hizo un pequeño estudio cuasi-experimental con cuatro pares de estudiantes de pre-grado variando
la condición de la disponibilidad de la calculadora. El análisis de los
protocolos de las sesiones revela que no hay mayores diferencias que se
puedan atribuir a la presencia o ausencia de la calculadora gráfica; sin
embargo, las diferencias observadas en el uso de estrategias que se usaron en cada problema pueden explicarse en términos de la naturaleza
del conocimiento en juego y de la disponibilidad de tal conocimiento para los estudiantes. El estudio sugiere además un modelo para realizar
investigaciones que busquen explicar los efectos de la tecnología en el
aprendizaje y en la instrucción.
Términos clave: Calculadoras gráficas; Funciones; Resolución de problemas
Graphing calculators have become part of high school mathematics classrooms.
A survey of calculator usage in high schools commissioned by the College Board
Mesa, V. (2008). Solving problems on functions: role of the graphing calculator. PNA, 2(3),
(Dion et al., 2001) indicated that graphing calculators are either required or allowed in at least 87% of the mathematics classes offered in high schools (p. 430).
This imposes an interesting challenge to both college mathematics teachers and
to mathematics educators who are responsible of preparing future mathematics
teachers, as many of their students may come with experience with graphing calculators from their high school. A review of the research involving graphing calculators at the undergraduate level shows at least two types of studies. On the one
hand, there are studies that investigate the impact of introducing graphing calculators in the classrooms on students’ motivation, attitude, achievement, and retention (Hennessy, 1997; Hollar & Norwood, 1999; Quesada & Maxwell, 1994;
Smith & Schotsberger, 1997). On the other hand, there are studies that investigate students’ understanding of the content or their discursive practices in the
classroom in relation to the representations offered by graphing calculators
(Dick, 2000; Kaput, 1992; Roschelle, Pea, Hoadley, Gordin, & Means, 2000;
Ruthven, 1990; Shumway, 1990; Slavit, 1994). Both types of studies work under
the assumption that the immediate availability of multiple representations of
mathematical objects facilitate the process of making connections among those
representations which in turn produces more robust or connected learning (Hiebert & Carpenter, 1992; Schoenfeld, 1987). However, using the graphing calculator efficiently in the classroom or documenting what actually is done with the
tool has proven to be more difficult to accomplish. Teachers’ beliefs and how
students organize themselves to work on problems have been cited as reasons
why implementations with graphing calculators do not work as expected (Demana, Schoen, & Waits, 1993; Simmt, 1997).
In this article I want to suggest that the nature of the tasks, students’ previous
mathematical knowledge, and their experiences with graphing technology—
independently of the availability of the graphing calculator—shape the collaborative construction of solutions among pairs of students. The present study was carried out to investigate the roles that the graphing calculator played when students
had controlled access to it in a problem solving session. Studies that look at large
effects of introducing the graphing calculator in classrooms (e.g., contrast overall
achievement of a group of students when technology is present vs. not present)
overlook the fact that the curriculum that is offered to each group is not comparable, and therefore it is not possible to conclude that differences in achievement,
attitudes, or retention could be attributed only to the presence of the graphing
calculators. And studies that look very closely at what happens when graphing
calculators are used in the classroom, can not attribute results to the presence of
the graphing calculator because there is not much knowledge about the particular
aspects related to how the graphing calculator is used in specially crafted situations or about how problems are solved without the graphing calculator. I contend that analyses of such situations may allow us to better understand which
outcomes can be attributed to the tool itself and which to other factors. Moreover,
a closer look at what students can do with and without the tool might better inPNA 2(3)
Solving Problems on Functions…
form the process of curriculum design and organization, as well as the quality of
the interplay between the graphing calculator, the content, and the students.
In this study, I wanted to know how pairs of students solved problems that
were produced under the assumption that multiple connections among representations of a mathematical concept strengthen understanding (Kaput, 1992) and to
determine how problems were solved under two different conditions, with the
graphing calculator and without the graphing calculator. I wanted to know if the
availability of the tool triggered questions that either guided their solutions or
changed their solution strategies, how much time students spent solving the problems under each condition, and the level of success solving the problems under
each condition. For the purposes of this article, I will report on answers to the
following three questions: (a) What strategies do the students choose as they attempt to solve a problem on functions? (b) How are the strategies different when
the graphing calculator is present and when the graphing calculator is not present? And, (c) what roles does the graphing calculator play in the solution process?
In the next section, I present a brief review of studies that may be seen as
paradigmatic of research with graphing calculators in general and of graphing
calculators in problem solving in particular.
PROBLEM SOLVING AND GRAPHING CALCULATORS
Reform documents such as the Curriculum and Evaluation Standards for School
Mathematics (National Council of Teachers of Mathematics, 1989) and Principles and Standards for School Mathematics (National Council of Teachers of
Mathematics, 2000) have put a strong emphasis on the use of technology within
the classroom. According to Smith (1998), teachers facing the challenge of introducing graphing calculators into their classrooms have used them in four distinguishable types of enrichments: tools for expediency (they save time used for tedious or difficult procedures), amplifiers for conceptual understanding (they offer
multiple linked representations), catalysts for critical thinking (they allow exploration of “what if” questions), and vehicles for integration with other disciplines
(they facilitate the work in other disciplines, like physics or programming). These
are a posteriori attributions that describe instruction with the tool; it is an open
question whether these attributed roles depend on the particular tool (the same
attributions can be made about a computer with a graphing program). A tool has
a limited set of functions and operations; it is the mathematical activities that impose conditions on how the tool is used in a given situation (see Balacheff,
1993). A main advantage of graphing calculators and computers is that they ease
teachers’ burden of creating materials, such as overhead transparencies for example. And between graphing calculators and computers, the “power of the small
and easy-to-use computer” (p. 1) does make an important difference in terms of
A typical experimental study contrasting the outcomes of two classes, one intact and one that had graphing calculators, is Graham and Thomas’s (2000).
Their study aimed at evaluating the potential of a unit designed to help students
understand algebraic variables2. The graphing calculator in the Graham and
Thomas’s study was taken as an amplifier for conceptual understanding, a catalyst for critical thinking, and as a vehicle for integrating other disciplines. The
study involved 147 students using a graphing calculator with the experimental
unit “Tapping into Algebra” and 42 students who followed their “normal algebra
teaching whole class, skills-based instruction and assessment with their usual
teacher presentation style” from six schools in New Zealand (p. 271). The activities used the graphing calculator storage variables: students would assign values
to these variables and afterwards conjecture what would happen after modifications (numerical operations) were performed on those variables. Another activity
consisted of students guessing the values of two variables when students knew
results of some numerical operations (e.g., A + B = 0 and A/ B = "1 , p. 270). According to Graham and Thomas, in these activities the students were involved
in a cybernetic process where the technology reacts to the individual’s
actions according to pre-programmed
rules. The environment provides consistent feedback in which students can predict and
test, enabling them to construct an understanding of letters in algebra
(emphasis in original, p. 270).
There was a statistically significant difference in performance between the two
groups of students—in favor of the experimental group—in a post-test that
measured “understanding of the use of letters as specific unknown, generalized
number and variable” (p. 272). The higher performance of the experimental
group was independent of prior student ability. Students in the control group statistically outperformed students in the experimental group in only one of the procedural skill items in the posttest (“simplify (a + b) + a ”). There were no significant differences between the two groups in other procedural items. From these
results, authors concluded that students “can obtain an improved understanding
of the use of letters as specific unknown
! or generalized number from a module of
work based on the graphic calculator” (p. 278). When no significant differences
are found between the two groups, as is in the case of the Smith and Schotsberger
(1997) and Alkhateeb and Wampler (2002) studies, the implication is that the
graphing calculator is not detrimental in terms of what students learn.
The argument of portability has become increasingly more popular among advocates of handheld computers (Vahey, Tatar, & Roschelle, 2004).
See also the following experimental studies at the college level: Alkhateeb and Wampler
(2002) on derivatives; Hollar and Norwood (1999) and Quesada and Maxwell (1994) on functions; and Smith and Schotsberger (1997) on college algebra.
Solving Problems on Functions…
These studies illustrate one of the main difficulties with experimental research on innovations that incorporate the graphing calculator: the control group
is never exposed to comparable mathematics as the experimental group, the assumption being that the control class should be intact. If one of the points at stake
is to know whether the graphing calculator makes a difference, a more appropriate approach (and for some, more ethical) to this kind of research would be one
in which the control group experiences a module that is not based on the graphing calculator but that preserves its spirit in every other aspect. If in this case, the
cybernetic process attributable to the presence of the graphing calculator does not
occur, statistically significant different results in performance in favor of the experimental group could be attributed to the absence of such cybernetic process.
One of the reasons that no studies have been conducted in such a way may be
that it is difficult to imagine what the dual environment might be. However, it
might be possible to deal with this fundamental problem.
The issue raised about the difficulty of conducting these studies is connected
to the types of activities teachers need to use when the graphing calculator is
available, because the activities assume a re-conceptualization of what it means
“to do mathematics”. But when doing mathematics is equated to problem solving3 (as it helps to build new knowledge, both within and outside mathematics,
National Council of Teachers of Mathematics, 2000, pp. 52-53) a natural question that arises is: How do graphing calculators support problem solving activities? A number of studies exist that describe ways in which graphing calculators
offer learning opportunities to students. Hennessy, Fung, and Scanlon (2001) reported several features of the technology that can structure and support collaborative problem solving. Their study dealt with undergraduate students using graphing calculators in an innovative course at the Open University that looked for fostering understanding of graphing. Drawing from Vygotsky’s ideas of tool mediation, from collaborative learning theory, and from work on gender and computing, they devised three design principles for the activities used (p. 269):
• Open-ended investigations in which graph recognition and interpretation are developed exploring different kinds of graphs.
• Personal ownership of the technology (one machine per student) and
of the activity (student choice of problem solving approach).
• Collaboration in planning and problem solving and ample opportunities for discussion with peers; use of individual machines to work on
a shared task; achievement of written consensus.
The course was aimed at building students’ confidence in using mathematics (p.
270). By the end of the course students reported positive feelings and attitudes
with respect to doing and learning mathematics, and an appreciation of the capa3
In this article a problem is defined as a task for which no clear pattern of solution exist.
bilities of the graphing calculator to visualize, to facilitate and accelerate computations, and to give immediate feedback (i.e., the cybernetic process described by
Graham & Thomas, 2000). In a follow-up study after four months of taking the
innovative course, students were observed “planning, executing, and reporting
calculator actions and strategies” (p. 275). The students used some trial and error,
but for most of the session, were confident and were not surprised by the results
obtained in the graphing calculator; they also engaged in productive explorations.
Students were observed performing actions that would not have been worthwhile
to conduct manually (e.g., checking if the graphs of two expressions superimpose). Hennessy et al. (2001) conclude that (a) the main advantages of the graphing calculator in facilitating students’ learning of graphing fall in three broad
categories: visualization of functions, automatic translation between representations and immediate feedback, and rapid and easy graph plotting (p. 278-279);
and (b) students working collaboratively was an important factor explaining the
richness of the problem solving session, because students were working synergistically (Noss & Hoyles, 1996) between interdependence and autonomy. The “use
of technology was firmly embedded within and inseparable from the mathematical activity being undertaken” (Hennessy et al., 2001, p. 282)4.
These studies highlight another difficulty for conducting investigations that
analyze the impact of the graphing calculator in a particular setting. Authors make explicit the principles that guide the design of the tasks used in the courses—
which also guides the design of the tasks for the case studies. Such principles,
backed up by strong theoretical frameworks, assist authors in producing activities
that help students develop certain mathematical notions with technology, with
technology and collaborative work playing a fundamental role. Researchers collect information about students on these aspects, and find that, not only do
students appreciate this way of teaching mathematics, but that in problem solving
sessions, they exhibit work in which the “use of technology [is] firmly embedded
within and inseparable from the mathematical activity being undertaken.” I wonder, could this be otherwise? Is it possible, after creating such a course, that the
use of technology was neither firmly embedded within nor inseparable from the
mathematical activity being undertaken? Or, that the students did not work collaboratively to solve the problems? The difficulty arises because more than the
presence of the tools—such as graphing calculator or collaborative work—it is
the quality and nature of the mathematics that is being learned that has dramatically changed. Our recognition of this different mathematics learning confounds
our appreciation of the actual role of technology and of group work.
The present study is a contribution to begin to understand these problems of
investigating the role that technology in general, and the graphing calculator in
See Figg and Burson (2005) and Roschelle et al. (2000) for similar studies that illustrate how
graphing calculators, computers, and handheld technologies can be used to improve the way in
which students learn.
Solving Problems on Functions…
particular, plays in problem solving sessions. The main questions—What strategies do students choose as they attempt to solve a problem on functions? How
are the strategies different when the graphing calculator is present from when the
graphing calculator is not present? And what roles does the graphing calculator
play in the solution process?—will be explored in a more “controlled” setting, as
is explained in the next section.
Two problems were adapted from Gómez and Mesa (1995), a book that contains
about 100 problems on pre-calculus. The problems are the result of a collective
work that studied the effects of introducing the graphing calculator in a precalculus course (Carulla & Gómez, 1996; Gómez & Fernández, 1997; Mesa &
Gómez, 1996; Valero & Gómez, 1996) and were created with the purpose of developing students’ higher-order mathematical thinking (Gómez & Mesa, 1995, p.
5), according to Resnick’s (1987) characterization (i.e., non-algorithmic problems having multiple solutions, a high level of uncertainty, and requiring selfregulation and a great deal of effort). Both problems required students to propose
expressions for functions that would satisfy a given set of conditions. Problem 1
provided symbolic forms for the functions with parameters for the coefficients of
the independent variable, and Problem 2 provided nine graphs of polynomial
functions, some of them related, with very few precise numerical referents (see
A full description of the content addressed, the reasons for choosing the
wording, predicted strategies, and difficulties, and the possible hints that could be
offered for overcoming the difficulties are described at length in Mesa (1996).
The problems do not preclude the use of solutions based exclusively on symbolic
approaches but symbolic approaches are not practical to find suitable solutions to
1. Find two functions, f ( x) = ( x " k )2 + k and g( x) = a x " b + c such that the
solution to the inequality f ( x) " g( x) is the interval [2,5] .
2. Give one expression for each of the functions shown:
Figure 1. Problems selected for the study (adapted from Gómez and Mesa, 1995,
pp. 93, 102-103)
The list of strategies, difficulties, and solutions that could emerge in the solution
process were compiled from previous experiences with the problems, and were
instrumental during the problem solving session to assist students during the session and for the analyses of the data that emerged.
Two versions of a problem-solving instrument were prepared. Each contained the two problems but with different instructions on the use of the graphing
calculator. In the first version, the students were not allowed to use the graphing
calculator in working Problem 1 but could use it for solving Problem 2. In the
second version, these instructions were reversed. The purpose of using two versions was to contrast the processes used when the graphing calculator was availPNA 2(3)
Solving Problems on Functions…
able with those when the graphing calculator was not available. The graphing
calculator used was the Texas Instruments TI-82.
A set of three questions—(a) How would things change if you were or were
not allowed to use the graphing calculator for solving each problem?, (b) What
knowledge do you think this kind of problems require?, and (c) What is your
opinion about the problems?—were prepared for having an informal discussion
once the session was over. With these questions I wanted to get the participants’
perspective on the problems and on the appropriateness of using the graphing
calculator in teaching mathematics.
Because of the nature of the problems I chose, I needed to carefully select the
participants in order to guarantee that the problems would not look too alien to
them. I asked permission to conduct in-class problem-solving sessions in two
courses for students majoring in secondary mathematics education at a large
southern university. I visited the secondary school mathematics curriculum class
and the research seminar in mathematics education class, classes taken by the
students in their third and fourth year in the program. During these in-class problem-solving sessions, I gave all the students a list of 9 problems that covered topics in linear, quadratic, cubic, quartic, and rational functions. I used these sessions for identifying participants and for fine-tuning the characteristics of the activities that would be undertaken during the research problem-solving session.
The participants were chosen for their engagement with the class activities, the
different ways in which they addressed questions, their interest in using the graphing calculator, and their willingness to participate in the study. I asked them to
participate as pairs to facilitate their interactions during the problem solving session. Four pairs of students, A: Alice and Amy, B: Ben and Bill, C: Cindy and
Connie, and D: Dina and Donna (all pseudonyms), were selected; all students
had worked together frequently in class, except for Dina and Donna, who had
worked together only occasionally. All students had completed the mathematics
requirements of their programs (precalculus, calculus, linear algebra, modern algebra, geometry, discrete structures, history of mathematics, computers and algorithms, higher mathematics, and problem solving); one participant, Dina, had also taken differential equations and complex variables. Other characteristics
(mathematical confidence and performance and interest in inquiring and extending problems and in using technology) were collected after students had agreed
to participate in the problem solving session. Most of the pairs of students were
similar in these characteristics, except for pair D, which had the most differences
among them, except that both students exhibited low interest in using technology.
The data were collected during the spring quarter of 1996. The design was quasiexperimental, with pairs of students solving both problems under different conditions. Each version of the problem-solving instrument was given twice, each time
to a different pair of students. Pairs A and C solved the first version; pairs B and
D solved the second version (see Table 1). Each session was recorded and videotaped with two video cameras; one captured the students’ use of the graphing
calculator—which allowed me to record their keystrokes—and the other captured
the students’ interactions and gestures.
Availability of graphing calculator on problems for each group
A: Amy and Alice
B: Ben and Bill
C: Cindy and Connie
D: Donna and Dina
Each pair of students was told that they had 50 minutes to work on both problems5, that I would ask them a few questions about the process at the end, that
they needed to talk “as much as possible,” and that I would interact with them
only if they asked for help. The students were told to work together, speaking
their thinking out loud, and were reminded of the time limitation, which was
thought of as a pressure for them to negotiate a plan for arriving at some solution.
I provided a TI-82 graphing calculator to each student when the calculator could
be used. Once the pair had finished the first problem, I collected all the written
material and gave them the second problem. Field notes were taken during the
session and I wrote a full report later. When students indicated that they had finished or when time was over for the session, I conducted the exit interview.
Each problem-solving session was transcribed and eight protocols obtained, one
from each pair solving a problem. The protocols were supplemented with information from the videotapes and my field notes. I produced detailed descriptions
of the students’ solution to each problem. In order to better identify differences in
The estimation of time was based on previous experiences with these problems. Pairs of students who took part of the reformed pre-calculus course described in Gómez and Mesa (1995)
required an average of 15 minutes to find suitable solutions.
Solving Problems on Functions…
the solution processes under the two different conditions and the roles that the
graphing calculator played when it was available, I combined two frameworks,
Schoenfeld’s (1983) and Artzt and Armour-Thomas’s (1990) to parse the protocols. Schoenfeld’s framework was used to determine main episodes in each protocol by identifying all the points in which managerial decisions were made (e.g.,
“Let’s graph this equation on the graphing calculator”), and Artzt and ArmourThomas’s operationalization of cognitive and metacognitive processes was used
to identify points of managerial decision that were not accounted for with
Schoenfeld’s framework (e.g., students’ asking for clarification). Once the episodes were defined, they were classified as belonging to one of the following
categories: read, analyze, explore, plan, implement, plan and implement, verify,
and new information and local assessment, using both Shoenfeld’s and Artzt and
Armour-Thomas’s description. By parsing the protocols, I was able to identify
different stages of the problem solving process followed by each pair with each
problem. The parsing and their pictorial representation were used to make claims
about similarities or differences between situations and to establish in which episodes the graphing calculator was most commonly used. Table 2 presents the definition of the categories used to classify episodes in each protocol.
Categories of the analytical framework used for parsing the problem solving
The student reads the problem; includes consideration of the
The student decomposes the problem into its basic elements and
examines the implicit or explicit relations between the givens and
goals of the problem. The student may simplify or reformulate
The student searches for relevant information that can be incorporated into the analysis-plan-implement sequence. He or she
uses different problem-solving heuristics, examines related problems, or uses analogies. Trial-and-error strategies are common.
The student selects steps for solving the problem and a strategy
for combining them that might potentially lead to a problem solution if implemented.
The student executes each of the steps defined in the plan. The
student’s actions are systematic and deliberate in transforming
the givens into the goals of the problem.
Categories of the analytical framework used for parsing the problem solving
This category comprises those episodes in which the student does
not make the plan explicit, but one can be inferred from the student’s deliberate actions.
The student evaluates the outcome of the work so far, for example, by a recalculation of the computations.
Points that can trigger a change in types of episodes. New
information points are items in which a previously unnoticed
piece of information–data or heuristics–is obtained or
recognized. Local assessment is an evaluation of the current state
of the solution at a microscopic level.
When either the new information or the local assessment produced a change in the character of the episode, the triggering
elements were categorized as transition.
Consistency in the coding of the episodes was established by inter-rater agreement; a graduate student in mathematics education, not attached to the investigation, parsed two protocols using the descriptions given in Table 2. The agreement
(measured as number of utterances classified as belonging to the same episode
divided by the total number of utterances of the protocol) was 90% in one protocol and 100% in the other.
The protocol parsing was used to determine whether there were patterns common
for the cases in which the graphing calculator was available as contrasted to the
cases in which the graphing calculator was not available. In general, the parsing
reflects a problem solving behavior that is consistent with that of novice problem
solvers (Schoenfeld, 1992): students read the problem, sometimes analyzed the
situation (about 35 minutes in total for all groups) but usually began immediately
to implement a solution (approximately 70 minutes in total for all groups) or to
explore an alternative (about 75 minutes in total), without making a plan explicit,
and rarely conducting assessment of overall progress (about 10 minutes total for
all groups) or verification of solutions (about 10 minutes for all groups). The
main differences observed were across problems, with Problem 1 having twice as
much time spent analyzing the situation (23 minutes in Problem 1 vs. 12 minutes
in Problem 2) and Problem 2 exhibiting considerable portions of exploration (70
Solving Problems on Functions…
minutes total for all groups) that did not happen in Problem 1 (5 minutes for only
one group). For more details about the parsing and the results, see Mesa (1996).
Given that the major differences seem to be associated with the problems, I turn
now to answer Question (a)—What strategies do students choose as they attempt
to solve a problem on functions?—by describing the solutions the groups proposed for each problem and then presenting the uses of the graphing calculator
by the groups that had it available. In the discussion section, I will present answers to the remaining two questions, How are strategies different when the
graphing calculator is present from when the graphing calculator is not present?,
and What role does the graphing calculator play in the solution process?
The Students’ Solutions
In solving Problem 1, two different solutions were observed: Fix parameters and
solve the equations, followed by pairs A, B, and C, and Solve inequalities symbolically followed by pair D. In the first strategy students created sketches of the
functions given and then selected arbitrary numbers for three of the parameters in
the expressions. Next, they set up a system of two equations and two unknowns
and solved for the two remaining parameters. A summary of the work by students in pair C, who did not have the graphing calculator available for this problem, is given in Table 3. Only one group attempted a verification of the solution
Solution to Problem 1 by Group C
They assigned values to h and k.
h = 3.5 and k = 0
They found f (2) by direct substitution in
f ( x) , and found f (5) by symmetry in
f (2) = f (5) = 2.25
Solution to Problem 1 by Group C
They assigned values to h and k.
h = 3.5 and k = 0
Next, they fixed b.
b = 3.5 ; therefore, y = a x " 3.5
They substituted y = 2.25 in the expres!
sion for g( x) and solved for a, using
both 2 and 5 in place of x.
x) = 1.5 x " 3.5
! strategy, followed by!pair D, who had the graphing calculator for
In the second
! problem, the students wrote explicitly the inequality, replaced x = 2 and
x = 5 into the inequality, and obtained a system of two inequalities:
a 2 " b + c " (2 " h) 2 " k # 0
a 5 " b + c " (5 " h) 2 " k # 0
Taking cases for the absolute value, they generated four inequalities, and after
some manipulations—some of them incorrect—reduced the problem to one inequality that related !two of the four parameters:
a 2 " b " a 5 " b # ( 4 + 4h + h 2 ) + (25 "10h + h 2 )
The students did not incorporate graphs or sketches of the functions into their solution and were unable to provide a pair of functions that satisfied the conditions
The solution by Group B, which had the graphing calculator, is worth discussing further, because this is the only group that attempted a verification of
their solution with the tool, a move that helped them to correct their original “solution” (see Table 4).
Excerpts from the solution to Problem 1 by Group B with graphing calculator
They wrote an inequality using the functions with (2 " h) 2 + k = a 2 " b + c
parameters and wrote two equations, one obtained by
(5 " h) 2 + k = a 5 " b + c
substituting x = 2 the other by substituting x = 5.
They substituted values for h and k (i.e., fixed
! the (2 "1) + 3 =
parabola by setting h = 1 and k = 3).
They set b equal to 2 and solved the resulting system 4 = a 2 " 2 + c
Solving Problems on Functions…
Excerpts from the solution to Problem 1 by Group B with graphing calculator
of two equations and two unknowns for a and c us19 = a 5 " 2 + c
ing the values of f (2) and f (5) getting a = 5, c = 4 .
They wrote down the expressions and entered them
into the graphing calculator: f (x) = (x "1) 2 + 3 ;
g( x) = 5!
x " 2 + 4. !
They were about to say that the problem was solved,
! in the graphing calculabut the wider range of points
tor and the fact that the table showed three points at
which the functions were equal helped them state
that there was an error. They went over the process
but could not say what might be done to fix the problem.
They constructed a table of ordered pairs for the
function of the absolute value and made its graph on
paper. With these data they verified that there was a
third intersection point.
After exploring with the graphing calculator they
found that moving the vertex of the function with the
absolute value away from x = 2 would eliminate the
They changed b to a number greater than 2 and 5,
! the two equations.
namely 8, and solved
Ben and Bill chose b, the x-coordinate of the vertex of the function with absolute
value, as one of the extremes of the solution interval and a parabola that was
“opening up.” These selections forced the vertex of the absolute value function to
be on the parabola, and “inside” it, but because quadratic functions grow faster
than linear functions, a third intersection could be expected. Both of the students
in this pair, however, were reluctant to accept the evidence shown in the graphing
calculator and created a table, finding values with paper and pencil, to confirm
the results. After some time spent in assimilating the evidence, students modified
the solution, by moving the vertex of the parabola to the right of the interval, and
repeating the process again. No attempt to verify this new solution was made.
In solving Problem 2, all the students began by recognizing the degrees of
the polynomials depicted; they also recognized the transformations that were applied to some of them to obtain others. Almost all the students’ solutions incorPNA 2(3)
porated elements of three different strategies to find a basic function that could
be transformed to obtain other graphs that were evidently derived from such
functions6. The three strategies were (a) transform a general expression, (b) use
roots observed to create factors, and (c) set up a system of linear equations. With
the first strategy students began with a polynomial expression (e.g.,
ax 3 + bx 2 + cx + d for a cubic polynomial) and using trial and error modified the
parameters (a, b, and c) to obtain a graph that resembled the one sought. With the
second strategy, students used the roots seen in the graphs to create a symbolic
expression in the form a( x " r1 )( x " r2 )…( x " rn ) , for a polynomial of degree n.
With the third strategy, students selected points on the given graphs and substituted them into a polynomial expression to set up a system of linear equations
that is solved by!standard methods.
Groups A and C, who had the graphing calculator, used the first strategy; that
is, they attempted to transform the general expression of a cubic function to fit
Graph 1. They started by assigning parameters to the expression
ax 3 + bx 2 + cx + 10 and checking the resulting graph against Graph 1. After unfruitful experimentation they asked for help. I suggested looking at the similarities between their graph and the graph of the expression y = x 3 " x . Finding the
two graphs similar, the students began to use trial and error in modifying this expression to obtain the expected graph. After more intervention aimed at making
explicit the relation between the roots of a polynomial
and the factored expres!
sion (e.g., “factor the expression and consider when the function is zero”), the
students in these two groups produced reasonable expressions for Graphs 1 and
2. Students who had access to the graphing calculator relied on the general shape
of the graph to appraise the good fit of their proposed functions, rather than on
the analytical tools that can be used to model the functions.
Of the two groups who solved this problem without the graphing calculator,
Group D, used the second strategy, using the roots and provided expressions for
graphs 4, 5, 6, 7, and 1 (in that order). Group B used the third strategy. Ben and
Bill started with a factored expression to set up a system of linear equations to
find Graph 1: They wrote three linear factors, f ( x) = "a( x + 2)( x " 3)( x " z) , multiplied them together to obtain the general cubic equation, and solved the system
of two equations in two unknowns [choosing f (1) = 7 and f (0) = 8 ], which led
them to the final expression:
The activity assumed that students would be generating the polynomial function of smallest
degree whose behavior in the given interval was the same as the behavior illustrated in the figures. Without this condition, we have infinitely many expressions that could fit each graph in the
Solving Problems on Functions…
f ( x) = "
( x + 2)( x " 3)( x " ) .
They made some computational mistakes in the process. The students did not attempt to verify the correctness of their proposal and did not find other expressions.
Students’ Use of the Graphing Calculator
It has been suggested that the graphing calculator is an important tool during exploratory work in problem solving activities. In this study, students seemed to
prefer to use the graphing calculator for verification in Problem 1 and for exploration in Problem 2. In solving Problem 1 only Group D used the graphing calculator in an exploratory way, to find out the shape of the absolute value function,
but did not use the result obtained nor did they attempt to use the graphing calculator again:
Okay, well let’s see what this [absolute value function] graph looks
like. Can you find what this graph looks like?
With this? [Pointing to the graphing calculator.]
[After some difficulties with the range of the display screen, Donna
produces the graph of g( x) = x "1 + 3]
That is what it looks like? Neat.
Well, it depends on what your constants are.
Right. Okay. [Returns to her symbolic manipulations]
Later, the possibility of using the graphing calculator is ruled out:
Donna: I can’t do the graph of them [in the graphing calculator] until we
Yes, what these numbers are!
Because the problem asked for five parameters and Group D did not know them,
they decided that using the graphing calculator was inappropriate. They felt that
they could not use what they were supposed to find as part of the solution. The
situation did not make it possible for them to use the graphing calculator as they
expected, in checking the correctness of the graphs of the functions that they
were looking for.
Group B used the graphing calculator in Problem 1 during a verification episode that in the long run allowed them to carry out some exploratory work. Recall that when conducting the verification they found three intersection points in-
stead of two and that grappling with the mistake was not straightforward. Their
first reaction was to deny the evidence and demonstrate that there was a mistake
in the information provided by the calculator. One argument used was that the
parabola was wide enough which would make a third intersection point unlikely
to occur. Ben checked, both by hand and by using the table option in the calculator, that the functions intersected at 2 and 5. When a wider window showed them
the three points of intersection, they drew by hand the graph of the absolute value
using their hand-generated table of ordered pairs. Once they were convinced that
the two graphs also intersected at –5, they proceeded to see how to resolve the
conflict in a cycle of explorations. But in spite of this cycle of explorations, Ben
and Bill, appeared to use the graphing calculator as a verification tool during the
problem solving session. The fact that the graphing calculator did not produce the
expected results and their reluctance to accept the evidence indicates that they
expected to check their answers with the calculator.
During the post-interview, the groups that did not use the graphing calculator
indicated that they would have used it in Problem 1 for checking their answers.
Group A said:
But the main point was the full process. As for where do you start and
where do you put that vertex and, you know, those things, you can’t
really use the graphing calculator with them yet. ... We kind of experiment if there were different parabolas than what we thought and then we
graph it quickly for us instead of doing it by hand. But I think that the
by-hand part helped us more than anything.
Or if we do not know what the absolute value graph was, we just can
graph a quick and easy absolute value and see what it was. But–.
But it is as easy to do it by hand as it is to do with the calculator.
Group C gave a similar response:
Connie: I think that having the graphing calculator, you can do it [solve the
problem], but playing around more.
You mean more trial and error?
Connie: Yeah, I don’t, you know, I usually do not think about what I am doing
when I am using it [the graphing calculator] a lot of the time.... I mean,
I do [think], but I draw less.…
This is more of a process, you know. We have numbers. We would be
more totally guessing [by using the graphing calculator].
Note, in this last excerpt, how the role of the graphing calculator in exploring is
downplayed (“totally guessing”, “more trial and error”). In contrast, in Problem 2
the graphing calculator was used principally in exploration episodes to test the
Solving Problems on Functions…
effects of different coefficient values on the graph of the function. Students who
had the graphing calculator tested many parameters and suggested several hypothesis about the connections between those parameters and what they were getting, very similar in what Graham and Thomas (2000) describe as a cybernetic
process. However, in the absence of the knowledge about the relation between
the roots of a polynomial as seen in its graphs and the polynomial’s factored expression most of these explorations were not productive. Although all four
groups made conjectures as to the value of the parameters, the graphing calculator made a difference in the time used to answer the same questions. On average,
the groups without the graphing calculator tested two functions in 3.5 minutes,
whereas the groups with the graphing calculator tested an average of five functions in 3 minutes. That the graphing calculator was expected to be useful in their
exploration was also evident by Group D’s “Oh! We can not use the graphing
calculator for this!” uttered with disappointment at the beginning of their work
on this problem. Pairs A and C who had access to the graphing calculator spent
about 46 minutes in explore episodes, 40 of which were done with the graphing
An important difference to note between the groups with graphing calculators and the groups that did not have them, was the time spent in solving the
problems. Table 5 shows the time in minutes each group spent working on each
problem. The underlined number corresponds to the session when the graphing
calculator was available: When the students had the graphing calculator, they
took more time—in one case twice as much as—than the time used when the
graphing calculator was not available.
Time in minutes to solve each problem by each
Note: The underlined number corresponds to the minutes
spent in the session when the graphing calculator was
Two key observations emerge from these results. There is not much reason to believe that there were substantial differences in how the students solved the probPNA 2(3)
lems when the graphing calculator was available as compared with when it was
not available. Second, the students opted for strategies in which the graphing calculator would back up their answers to solve Problem 1, or would test their hypothesis about the coefficients for Problem 2. The only advantage the students
with the graphing calculator had in solving Problem 2 was that they could test
more functions in the same amount of time.
Strategies Are not That Different
Although Problem 1 is not a standard inequality problem (such as find the solution to (4 x " 2) 2 "1 # 3 x " 2 + 2 ), it contained elements that might have induced
the recall of certain approaches (decompose the absolute value, find the points
when an equality is obtained). However, the task is designed in such a way that a
link between the symbolic representation of the functions and its graphical repre!
sentation is useful for progressing towards a solution of the problem. Failure to
use this link kept Group D from solving the problem. The graphical representation can come into play in two ways: in depicting how the functions look (something that all groups did) and in confirming that the inequality had the given solution set. For none of the groups was the graphing calculator crucial for depicting
the functions: Groups A, B, and C knew the form of the graphs they were dealing
with whereas Group D thought they did not have the right to use it. But for all the
groups, the graphing calculator was fundamental for verifying that the functions
satisfied the given conditions. Students who did not have access to the graphing
calculator were able to find a solution to the problem whenever they knew general shapes of the functions involved, and for at least one group the graphing calculator was important in verifying that the functions proposed satisfied the conditions given. A symbolic approach to verification would require selecting a representative value from three different sets (a number less than 2, another between 2
and 5, and another greater than 5) and to establish in each case whether the left
side of the inequality is greater than, equal to, or less than the right side. A
graphical representation simplifies this task by showing the intervals in which
one graph is above, coincides with, or is below the other. Thus it seems that because students in Groups A, B, and C could connect the symbolic and graphical
representations, they were able to engage in a process that led them to produce a
solution, whereas the lack of this link made it impossible for Group D to find a
solution independently of the availability of the graphing calculator.
The students in this study did not attempt to find other possible solutions to
Problem 1. From previous experiences, I have found that a visual representation
of the functions facilitates “dropping” conditions implicitly imposed on the solution (e.g., students tend to assume that the vertices of the parabola and the absolute value should coincide with the half point of the interval [2, 5], or that a in
function g(x) must be different from 0). The visual representation provided by the
graphs allows students to identify the two points of intersection as key for the solution: these points, and the relation between the functions, should be kept. Even
Solving Problems on Functions…
in environments when the graphing calculator is not available, students tend to
imagine how the vertex of the absolute value could be “moved up and down,”
satisfying the conditions and generating hypothesis about the values of the parameters. One such attempt could be perceived by Group B’s resolution of their
conflict when they “dragged” the vertex of the absolute value away from the solution interval. It might be possible that further probing could have helped these
students realize that there were many more pairs of functions that satisfied the
Problem 2 illustrates a different issue. The most difficult task for the students
was to produce a basic expression that could be transformed to generate a new
expression that would match the graph given. Students in groups A, B, and C
were confident with the transformations but lacked familiarity with the
mathematical content that would facilitate the production of a basic expression
for these types of polynomials (recall that none of these students had trouble with
linear and quadratic expressions). Students were resourceful in using their
knowledge (of general aspects such as shape and symbolic representation most
likely to produce such outcomes) either as a basis to begin an exploration of parameters or for finding alternate routes (e.g., setting up a system of equations) to
solve the problem. However, once the connection between the factored expression of a polynomial and the roots as illustrated in a graph was made available
for the students—that was Group D’s initial knowledge—the solution process
became more straightforward. Again, the availability of the knowledge was a better “predictor” of the outcomes than the presence or absence of the graphing calculator.
Roles of the Graphing Calculator
In both problems, the main use of the graphing calculator was to find the graph
of a function. Graphing a function, however, served different purposes in solving
the two problems: to verify solutions in Problem 1 and to test proposed values for
parameters in Problem 2. Students’ previous encounters with graphing calculators, the nature of the tasks, and the knowledge the tasks put in play may help
explain these results.
The graphing calculator allows students to work on problems in which a
family of functions is described based on results of multiple graphs of a parameterized expression (e.g., ax 2 + bx + c ). Another common use consists in looking
at the different representations available (tables, graphs, symbolic expressions),
altering them, and contrasting the results. Yet other uses involve the interpolation
of data to find expressions
that fit a given data set. All these tasks, in essence,
take advantage of what the graphing calculator can do, overlooking to some extent the questions of what mathematics is worth teaching given these capabilities
of the tool (Williams, 1993). Thus in Problem 1, in which the students had the
expressions and there was not an explicit requirement of describing the family of
functions depicted, students chose to use the graphing calculator to verify the soPNA 2(3)
lutions obtained. Problem 2, on the other hand, required students to re-create the
expression that would fit the graphs given. Even in this case, the students opted
for a parameter testing procedure applied to a given expression, taking advantage
of what the calculator can do. Students’ appraisal of the goodness of fit was
based on the graphical resemblance rather than on structural properties of the
The knowledge at stake, that is, the knowledge that is needed in order to be
able to engage productively in the activities, may also explain the different purposes for graphing a function. In Problem 1, Groups A, B, and C were familiar
with the general characteristics of the functions and with the relationship between
the solution to an inequality and the graphical representation of the expressions
involved. Group D lacked this knowledge; therefore, they could not advance productively in solving the problem in spite of the availability of the graphing calculator, and in spite of their using it in a brief exploratory episode. However, for all
groups, familiarity and confidence in their solution process made them use the
graphing calculator in verifying their solutions. Only when the graphing calculator gave results that were unexpected, did the act of graphing have a different
purpose. In Problem 2, the knowledge at stake, that is the fundamental theorem
of algebra, was not in students’ repertoire. In this case, the function-graphing capability of the graphing calculator became the only resource students could use
more or less successfully, in order to deal with the problem. Interestingly, once a
basic function was found, the process of transforming the function to obtain related functions became a paper and pencil activity, with the graphing calculator
playing a very limited role in verifying that the functions proposed met the expected conditions.
Finally, how students have experienced solving mathematical problems with
the graphing calculator may account for students’ use of the tool. The students in
this study had a varied set of experiences with and interest in using the graphing
calculators, but such diversity does not seem to have had an influence on the uses
they gave to the tool—the uses seem independent of such experiences. A larger
sample of students may be more suitable for establishing the extent to which previous experiences with the graphing calculator can affect how the graphing calculator is used in a problem solving session.
The results of this study suggest that the role of the graphing calculator in guiding students’ active construction of solutions to problem solving activities—at
least when they relate to functions—merits more careful analysis. There is some
reason to believe that what guides the problem solving efforts beyond the availability of the tools is (a) the way in which knowledge is to be used and (b) students’ familiarity with that knowledge.
Solving Problems on Functions…
The students in this study were not trained to use the graphing calculator in
particular ways; nor did they declare a heavy use of graphing calculator as undergraduates. This fact adds to these findings, because it shows that students were actually responding to the constraints in the tasks and not to the cues that may
be established when there is an instructional agenda that supports the use of
graphing calculators as in the studies reported in the literature review section. Indeed, it is difficult to control for instruction in such studies; but instruction
should be taken into account when making claims about the effects of innovations in the classroom. On a recent observation, a group of about 15 second-year
undergraduate students were given Problem 1 after receiving a 5-minute refresher on linear and quadratic functions. The refresher was delivered using the
blackboard and overhead projector and transparencies that were moved up and
down to illustrate effect of parameters on linear functions. In spite of the availability of the graphing calculator, none of these students opted for using it; they
arrived at solutions in less time than what was observed in this study; and were
able to suggest families of solutions, to generalize, and drop some of the conditions. My short “instruction” in this case pointed at the relevant knowledge students needed to be able to engage with the task. So, in this case, with students
who were not familiar with the problem the overhead projector and the transparencies fulfilled the purpose of illustrating the connections between representations, which was a crucial content for attempting the problem. Thus the claim
that graphing calculators can be used as amplifiers for conceptual understanding,
as catalysts for critical thinking, or as vehicles for integration, as suggested by
Smith (1998), is a claim that may apply to other tools, given that instruction,
with those tools, includes such purposes.
Smith also notes that graphing calculators are seen as tools for expediency,
because they save time that otherwise would be used in tedious or difficult procedures (e.g., estimating a best fit regression line). However, the results of this
study suggest that the time saved in computations will be used in other kinds of
activities that may be more time consuming. The illustration for this phenomenon
is the time spent in exploration by the groups who had the graphing calculator in
Problem 2: of the 65 minutes that the groups spent solving Problem 2, 46 minutes (71%) were devoted to exploration; and of these 46 minutes, 87% was devoted to exploration with the graphing calculator. This is a sizable amount of
time, especially considering that for the most part the explorations did not help
students find suitable solutions, until an intervention helped students reorganize
their exploration (“factor the expression y = x 3 " x and consider when the function is zero”). This phenomenon raises an important issue for practice because it
shows that even though the cybernetic process is a key feature that the graphing
calculator affords, if the process
is not informed by mathematical principles,
knowledge reorganization may not occur after all.
That students used the graphing calculator to verify the solutions when they
were more comfortable with the knowledge and to explore when they were not,
may give some root to the perception that real mathematics is not really carried
out with calculators, that the mathematics that counts is the one that is done with
paper and pencil. Thus in spite of the importance of the tool for assisting in finding solutions, the tool is after all, a crutch, something you may dispose of when
you become proficient with the knowledge. Further research in this area could
consider students’ beliefs about the role of technology in doing mathematics.
Finally, this study was sensitive to offering the same mathematical opportunities to both groups of students; that is it looked for maintaining the mathematics at stake a “constant.” Ethical issues may arise when we conduct studies that
benefit some and not other groups of students, as it may happen in controlled experiments in education. However, the issue of crafting activities that put interesting mathematical knowledge at stake is a difficult one, more so when we also
want to study the actual role that tools play in those situations. The alternative
proposed in this study was to not have the tool, and to study that situation vis a
vis the situation in which the tool was present. Could we claim that there was not
a cybernetic process? Or that there were not opportunities for conceptual understanding? Or that critical thinking did not happen? As illustrated by the results,
these activities happened in both situations. Thus the tool itself, although it may
have been seen as the reason for some of these opportunities to happen, may only
be playing a secondary role to that of the transformation of instruction and curriculum. The study of these transformations may be a more fruitful area for further investigation of students’ uses of technology in the classroom.
This paper is based on the author’s master’s thesis completed at the University of
Georgia under the direction of Jeremy Kilpatrick. Many thanks to Jeremy Kilpatrick, Dan Chazan, Patricio Herbst, Edward Silver, Jack Smith, and two anonymous reviewers for comments to previous versions of the paper.
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This document was originally published as Mesa, V. (2007). Solving problems
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Mathematics, 29(3), 30-54.
University of Michigan


Fractional Calculus and Fractional Stochastic Calculus, with Applications
There are two distinct prominent topics in the theory of stochastic processes: the theory of fractional processes (including stochastic differential equations and stochastic partial differential equations with respect to fractional processes), and the theory of stochastic differential equations and stochastic partial differential equations with fractional operators. These topics are connected via fractional calculus. Fractional calculus is deterministic (not stochastic), and is simply a specific set of tools and methods. However, mastering these methods allows one not only to consider the analytical properties of the solutions of the above equations, but also to apply them to problems in physics, mechanics, functioning of communication systems, finance, economics, statistics, and many other applications.
The proposed course of lectures is precisely aimed at explaining the mathematical arguments of fractional calculus and fractional stochastic calculus, including rough-path theory, with subsequent application to stochastic differential equations (SDEs), stochastic partial differential equations (SPDEs), and finance. It will consider fractional processes as well as the theory of stochastic differential equations and stochastic partial differential equations with fractional operators, and study the connection between the two, with application to finance.
This course is aimed at a broad mathematical audience interested in working with long- and short-memory processes. In particular it will be beneficial for PhD students. The course will be accompanied by exercise sessions.
Fractional Calculus and Fractional Stochastic Calculus with Applications
On some connections between martingales and fractional derivatives
Probabilistic point of view on generalized fractional calculus and generalized operator-valued Mittag-Leffler functions.
Lecture on mulitfractality
Most likely location of paths for a class of Feller processes
Modelling the effects of data streams using the Logsig-RNN model
Fractional non-homogenous Poisson process: characterisation and limit theorems.
University of Kyiv
Yuliya Mishura has been working in Taras Shevchenko National University of Kyiv, Ukraine since 1976, becoming a professor in 1991. She has been the head of the Department of Probability, Statistics and Actuarial Mathematics since 2003. She is also the head of the research group “Exact formulas, estimates, asymptotic properties, and statistical analysis of complex evolutionary systems with many degrees of freedom."
At present the research interests of Y. Mishura lay in the area of the interplay between fractional stochastic processes and differential equations with respect to fractional operators, where random processes arise. However, the random processes which arise from solving fractional differential equations are very different from fractional stochastic processes. Luckily the fractional calculus happened to be the connecting bridge between them. The purpose of the proposed course is to present both an exposition of the basic ideas in the theory of fractional stochastic processes and the main ideas from the theory of differential equations with fractional operators. Both these areas at the moment are very popular and are rapidly developing, speeded up by a large number of applications.
Brunel University London
University of Warwick
University of Sussex
Get in Touch
Kingston Ln, London, Uxbridge UB8 3PH, UK


How to use Mass Percent Calculator?
You can see two input fields labeled “Mass of Compound (g)” and “Mass of Element (g)”.
- Enter the mass (in grams) of the entire compound in the “Mass of Compound (g)” field.
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The result will be displayed below the “Calculate” button in the “Result” section. The mass percent will be displayed to 2 decimal places.
Note: The mass percent calculator uses the formula “Mass Percent = (Mass of Element / Mass of Compound) * 100”.


Predictive performance of 12 equations for estimating glomerular filtration rate in severely obese patients
Considering that the Cockcroft-Gault formula and the equation of diet modification in renal disease are amply used in clinical practice to estimate the glomerular filtration rate, although they seem to have low accuracy in obese patients, the present study intends to evaluate the predictive performance of 12 equations used to estimate the glomerular filtration rate in obese patients.
This is a cross-sectional retrospective study, conducted between 2007 and 2008 and carried out at a university, of 140 patients with severe obesity (mean body mass index 44 ± 4.4 kg/m2). The glomerular filtration rate was determined by means of 24-hour urine samples. Patients were classified into one or more of the four subgroups: impaired glucose tolerance (n = 43), diabetic (n = 24), metabolic syndrome (n = 76), and/or hypertension (n = 66). We used bias, precision, and accuracy to assess the predictive performance of each equation in the entire group and in the subgroups.
In renal disease, Cockcroft-Gault’s formula and the diet modification equation are not precise in severely obese patients (precision: 40.9 and 33.4, respectively). Sobh’s equation showed no bias in the general group or in two subgroups. Salazar-Corcoran’s and Sobh’s equations showed no bias for the entire group (Bias: −5.2, 95% confidence interval (CI) = −11.4, 1.0, and 6. 2; 95%CI = −0.3, 12.7, respectively). All the other equations were imprecise for the entire group.
Of the equations studied, those of Sobh and Salazar-Corcoran seem to be the best for estimating the glomerular filtration rate in severely obese patients analyzed in our study.


St. Nicholas School offers a very challenging academic program, which exceeds the California State Standards. We offer all of our students the opportunities that a gifted program offers. The strong foundations established in reading and math for the primary grades allows our students to proceed at an accelerated rate as they progress through each grade level. By the time our students reach sixth grade, they are using English and math textbooks one grade level ahead of their current grade. Our emphasis on developing good work habits and study skills allows our students to achieve scores well above the national averages (see Stanford Achievement Test Results).
Instruction is planned to include activities in all learning modes: auditory, visual, and kinesthetic. Students are encouraged to be active learners by participating in class. Projects in all areas of study are also planned to further enrich their learning, while allowing students opportunities to express their creativity and to practice their critical thinking skills. Good penmanship is also included as part of the curriculum to teach students the importance of presentation skills and to develop a sense of pride in their work.
Even our grading scale reflects the high standards our students attain:
- A = 93–100%
- B = 85–92%
- C = 74–84%
- D = 65–73%


Count with little fish by Lucy Cousins
Walker Books, 2018. ISBN 9781406374193
(Age: 2+) Highly recommended. Board book. This is a colorful counting book that has both the numerals and the number word and a little rhyme to go with each number.
This is a fun counting book from 1 to 10.
The bright colors engage the reader and the slightly tactile pictures of the fish make it fun to find them on the page.
The numerals are nice and big to be able to trace over with small fingers.
I highly recommend this book for 2+


Beth Greene Costner named Dean of the Richard W. Riley College of Education at Winthrop University
From the website of Winthrop University
Beth Greene Costner has been named dean of Winthrop University’s Richard W. Riley College of Education following a national search. Costner has served as interim dean of the college since January.
Provost Adrienne McCormick shared that Costner is highly regarded by the students, faculty, and staff of the Richard W. Riley College of Education. “Our education and exercise science programs are all leaders in the state,” McCormick noted. “Costner's extensive experience in management, assessment, accreditation, grant-writing, interdisciplinary collaboration and student success programming will position us well as we identify ways to continue strengthening our diverse programs in the college.”
Costner earned tenure and was promoted to professor in Winthrop’s Department of Mathematics where she also served as chair from 2008-13. In addition to her recent role as associate dean in education, Costner served as assistant and associate dean in the College of Arts and Sciences from 2010-15.
Costner started her career in education in 1992 as a middle grades mathematics teacher in the Clark County School District in Kentucky. She earned both a B.S. in mathematics education and M.S. in middle grades mathematics from the University of Kentucky, and a Ph.D. in mathematics education from The Ohio State University. Costner’s teaching and research has been focused on the content knowledge development of elementary and middle grades teachers.
While at Winthrop, Costner has secured more than $3 million in grants, including the Winthrop Initiative for STEM Education, which is a National Science Foundation Robert Noyce Scholarship project that supports the recruitment, retention and development of science and mathematics teachers.
In addition to efforts directed toward teacher development, Costner has led a number of initiatives related to student services, assessment and accreditation. In the area of accreditation, she served as the lead author on the most recent review of the educator preparation programs at Winthrop that resulted in reaccreditation in 2020 with no areas for improvement.


"I'm a proud Longmont native and St. Vrain Valley Graduate. I received my undergraduate degree Suma Cum Laude from UNC - Greeley and then my masters in Math Education from Montana State Bozeman. I've been teaching since 2009 and have been at Niwot High since 2012. I'm currently teaching Algebra 1 and AP Calculus. I am passionate about bringing the inner mathematician out in all my students and am proud to be apart of the Niwot High School community!
My husband Travis, also a SVVSD teacher, and I have been blessed to raise our two kids: Silas (3) and Charlie (2) as 4th generation Longmont natives (but no, we're not the 'farmer' Schlagel's). We enjoy all things CO: camping, hiking, 4 beautiful seasons, Rockies baseball and Broncos football. My heart is full of joy and I am so grateful, thankful and blessed."


Known for its solid presentation of mathematics, this bestseller is a rigorous but accessible introduction to both quantum chemistry and the math needed to master it. Quantum Chemistry, Seventh Edition covers quantum mechanics, atomic structure, and molecular electronic structure, and provides a thorough, unintimidating treatment of operators, differential equations, simultaneous linear equations, and other areas of required math. Practical for readers in all branches of chemistry, the new edition reflects the latest quantum chemistry research and methods of computational chemistry, and clearly demonstrates the usefulness and limitations of current quantum-mechanical methods for the calculation of molecular properties.
Please note that this is a eBook PDF digital format and not a hardcover printed book and the PDF file will be sent to your email once the payment has been made and it can be read in all computers, smartphone, tablets etc. The digital book will be sent to your email address within 12 hours.


Monday- TCAP Review
Tuesday - TCAP Review
Wednesday - TCAP Review
Thursday- TCAP Review
Friday - TCAP Review
Weekly Homework - Weekly Homework - TLW turn AM Practice. This assignemnt is due Tuesday, March 13th. New Weekly Homework: TLW add and subtraction fractions by doing WB pages 349 - 350 with HMH. This assignment is due Tuesday, March 20th.
Computer Programs: TLW should be on Level 38 of Math Facts. TLW work on Moby Max Math Fluency. TLW work on IXL sections A1 - 5 and 7 B, C, D, E1, G, H, I, K, L, and U.
If your child is absent and you would like to pick up make up work, please call the school, 788-6713, that morning. We will have work in the office after 2:00 for you to pick up.
Folders will be sent home each Tuesday during the school year. The following items will be included in the folder:
Folders are due back at school no later than Thursday. If a folder is not back by Thursday, the student will receive a check.


If you’re a parent of a young child who is almost ready for pre-k or kindergarten, then you’ve probably spent a lot of time thinking about how you can help them prepare to do well in school. Specifically, you’ve thought about the skills your child needs before they enter the classroom so that they get the most out of their education.
You know that reading and writing top the list of important academic skills, and you’ve likely taken steps to lay the foundation your child needs to develop their language skills. You read with them, you’ve taught them to hold a crayon or pencil, you've begun associating certain letters with certain sounds, and you are always talking with them, which helps them learn new vocabulary and develop conversational skills. All of this means that you are spending hours each day helping your child develop the language skills that they will need in school.
But how many hours do you dedicate to helping your child learn math? The answer for most parents - not many!
The Importance of Early Math
Research tells us that early exposure to math is one of the most important contributors to future academic success. Math helps children develop the confidence, curiosity, flexibility, inventiveness, and persistence that they will use throughout their education and their lives.
Free Downloadable Guide: Take a Closer Look at Friends' Central School
The earliest years are very important when it comes to laying this critical foundation, and that means that exposure to math should ideally begin before the child is enrolled in school. Because most parents aren’t trained math teachers, we wanted to pull together some advice that you can use to help your child develop confidence, familiarity, and—eventually—a love of math.
Which math skills are the most important for young children to learn? While it might be nice to daydream about your three-year-old becoming a whiz at advanced calculus, the truth is, just embracing the basics can help you set your child up for success. Some of the most important math skills for young children include:
- Number Sense: Knowing what individual numbers look like and understanding that certain numbers are larger (or smaller) than others
- Ordering: Understanding how numbers fall along a scale, for example from 1 to 10 or from 1 to 20
- Spatial Sense: Being able to identify different shapes and spaces that will later be used in geometry
- Pattern Recognition: Being able to identify repeating patterns, whether in numbers, shapes, music, art, or elsewhere
- Measurement: Understanding that numbers (and the world around us) are measurable and that there are ways to quantify them, for example with a ruler or with measuring cups
- Operations: Having an early understanding of basic operations (addition, subtraction, multiplication, division), as applied in real life ways
4 Ways Parents Can Help Their Young Children Love Math
Knowing that your child should learn these skills is one thing. Actually teaching them the skills is another thing entirely—and it can be easy for parents to feel a little overwhelmed when it comes to teaching their child about math. You aren’t a math teacher, after all!
Luckily, you don’t have to be. Below, we’ve gathered advice and tips that you can use to help your child learn not only how to do math, but also how to love it.
1. Find ways to incorporate math into everyday life.
Just like reading, writing, and speaking help your child to develop the language skills that they will use throughout their life, so too, does regularly practicing math skills.
But that doesn’t mean sitting your child down with worksheets. The single best way to help your child learn the basics is by incorporating math into their everyday activities. This will give your child the practice that they need, while also helping them to understand that there are real-world applications to what they are learning (which will come in especially helpful when they are older).
Some easy ways to build math into the everyday include:
- Asking your child to set the table, and asking them how many of each item (forks, spoons, plates) is needed
- Asking your child to divide different fruits (like grapes) into baggies for snack time
- Asking your child to help you measure (and count) ingredients when cooking or baking
2. Find ways to make math fun.
As a parent, you know that any time you call something work, your child may instinctively dislike it. But if you can frame something like work or learning as being “fun,” then there’s a much better chance that they will enjoy it.
Though “math” and “fun” may seem like two words that should never be used in the same sentence, there are plenty of ways that parents can build math into the games and activities that their children are already playing and completing:
- During playtime, ask your child to count out and retrieve a certain number of toys.
- Use Legos or another block to build different shapes and structures.
- During a long car ride, play I-Spy or “Guess the Number in My Head,” both of which can help your child reinforce counting skills while also laying some foundation around logic.
- When at the museum or aquarium, ask your child to count the number of circles in a painting or the amount of fish in a tank.
What about math-based video games? While they won’t hurt, they also aren’t best for young children, who are ideally taught by interacting with the world around them. Save the math-based video games for when your child is a little older, when they see the games as a treat.
3. Use real math language in daily life.
When it comes to teaching your child about math, it is hard to overstate the importance of using real math language in daily life. Just as using a variety of words when you speak to your child helps them build their vocabulary, using math-related words and terms can do wonders for their math skills. Doing so will help your child to become familiar with the words and concepts that they will need to use later on in school, and this familiarity can help them to build the confidence that is so important in education.
For example, you might ask your child to:
- Add up the forks on the table.
- Subtract extra plates if you’ve laid out too many.
- Divide the grapes into baggies.
- Multiply one table setting by four so the entire family can eat.
4. Resist talking negatively about math.
Even if you don’t like math, don’t think you are good at math, or didn’t do well in math as a child, you shouldn't speak negatively about math with your child. Doing so can drill in a negative perception that they can then carry with them throughout their lives.
Instead, when you talk about math, try to do so positively. Rather than doing math reluctantly, share with your children what a wonderful tool and skill math can be to helping them achieve their goals. Show your child from an early age that math is important, and they will naturally want to learn about it and do better.
And, remember: There is no such thing as a “math person”—everyone can learn to love math!
It’s not about memorization
In math, as in any subject, it’s easy for parents, teachers, and children themselves to become focused on being “right.” But while getting the correct answer is, of course, one of the goals of learning, putting too much focus on it can lead to a mindset that values the answer more than the process. Focus on teaching your child the underlying process and logic—and not simply memorizing numbers, facts, and operations—and everything else tends to fall into place.


If I use freely provided software to create a graph, then if copyright applies at all, how can it possibly apply except to say that the graph is mine?
But then the counter argument perhaps becomes: If an artist or draftsman says he will draw graphs to your specifications for free, then if copyright applies at all, how can it possibly apply except to say that the graph is his?
What a conundrum! It's almost as if monopolies on reproduction aren't well suited to the realities of non-physical works...
Perhaps it could be argued that a graph of a certain relation of values is a "performance" of the natural facts of the statistics and mathematics involved. That way, copyright would apply, just as it does when an orchestra records music itself in the public domain.
But the orchestra's copyright is only on works that they actually record. This claim seems to be asserted on all possible displays the software is able to produce, which would be overreaching indeed. In fact, I can't imagine how their claims could reach further.


Math 131 Projects 1-4 Each project is worth 2.5% of your final grade. CSUDH
Elementary Statistics and Probability Dr. Sally Moite
Format for projects
Title, your name and the text of the report on the first page (no title page).
No more than two pages, typed double spaced. No less than two paragraphs.
Write your report in complete sentences and paragraphs.
Answer as many of the questions below as possible in the text of your report.
In your report give the source you use (newspaper, web site, book, etc.)
Give the original source of any statistic, and/or the agency that collected it.
Include, as noted below, photocopies or copies of key parts of your source.
There should be no duplicate topics in the class.
Project 1 – Portrait of a statistic – Due Thursday Oct 3
Choose one statistical variable with numerical values that are reported periodically (yearly or monthly, for example) – this is called a time series - to describe in detail. (An example of a time series is the ozone hole area table at the bottom of page 35.) What is your statistic called, what does it measure or represent (describe this carefully), how often is it reported, what level does it cover (world, U.S. or other country, state, local)? Some examples are world population, U.S. GDP, California divorce rate, L.A. county home prices, leading major league batting average. Where is the statistic originally published, what company, agency, department or person collects the data, how is the data collected, compiled or calculated? Describe the statistic’s trend over time, recent behavior and latest reported level. If you find the information, describe any changes in the definition of the statistic or collection method over time, why and for whom the information is collected and published, how accurate the data is. Include a copy of (no more than one page of) a table (not a graph) that gives values of your statistic. Statistical handbooks can be found in the school or local library. Many statistics are available online, from the UN, US and other sources.
Project 2 – A graph of a statistic – Due Thursday Oct 17
Find a graph of statistical data in a newspaper, magazine or journal article or online news source. Carefully describe what data is presented. Does it come from a poll or survey, or from a census of a whole population? What type of graph (line graph, pie chart, bar chart, scatter plot) is used? Is the graph possibly misleading, for example, does any axis start from a number other than zero, or change scale? Give an example of how you read and interpret data from the graph by describing carefully and fully a point or a few points on the graph. What conclusion do you draw from the graph? Where did you find the graph? What information is given about the source of the data? Include a copy of the graph.
Project 3 – Two related statistics – Due Thursday Nov 12
Find data on two related variables for some type of entity, for example, population and GNP by country, median income and average age by zip code, auto accident rate and speed limit by state (not by year or time period). (An example of such cross-section data is the airline table on page 101.) Select 15 of the entities (countries, zip codes, or states) and list the selected entities and pairs of data. In your report, include the name of your source of data, a short description of each of the two variables, and how you selected your data, along with your list of entities and data. Enter the 15 values of the two variables into a statistical or spreadsheet program, make an xy-plot (scatter plot) of the data, and attach it to your report. Use the statistical program or your calculator to find the correlation coefficient. Does there appear to be a linear relationship between the variables? Include a copy of one page of the original data you used for the report or two pages if the data appear in separate tables.
Project 4 – A Statistical Study – Due Tuesday Nov 21
Find a study in a journal (possibly related to your major), magazine or newspaper that describes a statistical study in some detail. Give the source for this study. The study should give a confidence interval(s), statistical error estimate(s), or significance level(s) for data collected in the study. (The study should not be an exercise for a statistics class.) Briefly describe how the study was done and what person or group carried out and/or funded the study. (You may describe a part of a long article.) What variable was observed or measured? How large was the study group or sample? What population parameter, if any, was estimated? What conclusion was reached, and how was the accuracy of the conclusion described? What statistical test or technique was used? Include the article, or an abstract or summary for a long article (no more than three pages).


Hat Matrix Least Squares
Some simple properties of the hat matrix are important in interpreting least squares.
Hat matrix least squares. The OLS estimator exists and is unique ie. Least Squares Solution The matrix normal equations can be derived directly from the minimization of. John Fox in Encyclopedia of Social Measurement 2005.
So y ¼ Hy þMy ¼ yþe where because of 311 and 313 y0e ¼ 0 so that the vectors y and e are orthogonal to each other. If you have fitted your least square problem orthogonal methods like QR factorization and SVD then hat matrix is in simple form. That is y Hywhere H ZZ0Z 1Z0.
Therefore when performing linear regression in the matrix form if hatmathbfY. Tukey coined the term hat matrix for Hbecause it puts the hat on y. A least-squares solution of the matrix equation Ax b is a vector K x in R n such that dist b A K x dist b Ax for all other vectors x in R n.
Its least squares from a linear algebra point of view and adapted from Friedbergs Linear Algebra. You may check out my answer Compute projection hat matrix via QR factorization SVD and Cholesky factorization for explicit form of the hat matrix written in LaTeX. For weighted least squares we want Q1 and Q2.
The discrepancy is due to differences in how the software estimates the hat matrix on. 13 Idempotency of the Hat Matrix H is an n nsquare matrix and moreover it is idempotent which can be veri ed as follows HH XXT X 1XT XXT X 1XT XXT X 1XT XXT X 1XT XXT X 1XT H. Unfortunately estimating weighted least squares with HC2 or HC3 robust variance results in different answers across Stata and common approaches in R as well as Python.
In least-squares fitting it is important to understand the influence which a data y value will have on each fitted y value. Clearly there holds H0 ¼ H H2 ¼ H H þM ¼ I and HM ¼ 0. This approach also simplifies the calculations involved in removing a data point and it requires only simple modifications in the preferred numerical least-squares algorithms.
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Marc Chaikin is a developer of Accumulation Distribution Line and also Chaikin Money Flow indicator. Chaikin Money Flow indicator measure the volume of money flow over time that simply sums Money Flow Volume for 20 or 21 days.
Calculation based on 21 days.
1. Money Flow Multiplier = [(Close - Low) - (High - Close)] /(High - Low)
2. Money Flow Volume = Money Flow Multiplier x Volume for the Period
3. 201-period CMF = 201-period Sum of Money Flow Volume / 20 period Sum of Volume
Interpretation of Marc Chaikin is based on oscillator. A move into positive chart indicates buying pressure, while a move into negative chart indicates selling pressure. You should add buffer below and above zero line to avoid noise signal. You can set threshold a little above zero (+0.05) for buying signal and below zero (-0.05) for selling signal to reduce bad signal and reduce whipsaws. You can use Chaikin Money Flow as supporting indicator for RSI and MACD.
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