# (2013), "University students` grasp of inflection points,"

```Educ Stud Math
DOI 10.1007/s10649-012-9463-1
University students’ grasp of inflection points
Pessia Tsamir &amp; Regina Ovodenko
# Springer Science+Business Media Dordrecht 2013
Abstract This paper describes university students’ grasp of inflection points. The participants
were asked what inflection points are, to mark inflection points on graphs, to judge the validity
of related statements, and to find inflection points by investigating (1) a function, (2) the
derivative, and (3) the graph of the derivative. We found four erroneous images of inflection
points: (1) f ′ (x)=0 as a necessary condition, (2) f ′ (x)≠0 as a necessary condition, (3) f ″ (x)=0
as a sufficient condition, and (4) the location of “a peak point, where the graph bends” as an
inflection point. We use the lenses of Fischbein, Tall, and Vinner and Duval’s frameworks to
analyze students’ errors that were rooted in mathematical and in real-life contexts.
Keywords Inflection point . Concept image . Representation . Definition . Intuition
1 Introduction
The notion of inflection points is frequently discussed when dealing with investigations of
functions in calculus. Calculus is an important domain in mathematics and a central subject
within high school and post-high school mathematics curricula. In the literature and in our
preliminary studies, we found a few indications of learners’ erroneous conceptions of the
notion (e.g., Ovodenko &amp; Tsamir, 2005; Tall, 1987; Vinner, 1982). While the findings shed
some light on students’ grasp of inflection points, it seems important to further investigate
students’ related conceptions and to examine potential sources for their common errors.
In this paper, we examine university students’ conceptions of the notion of inflection
points, and we also use the context of inflection points to examine students’ proofs
(validating and refuting) when addressing inflection-point-related statements. In the field
of mathematics education, there are several theoretical frameworks proposing ways to
analyze students’ mathematical reasoning; yet usually, research data are interpreted in light
of a single theory. We believe that the use of different lenses may contribute to our
interpretational examinations of the data and may offer us rich terminologies to address and
to analyze the findings (e.g., Tsamir, 2007, 2008). Thus, we offer a range of interpretations of
students’ conceptions based on three theoretical frameworks which are widely used to highlight
possible sources of students’ difficulties in mathematics: Fischbein’s (e.g., 1993a) analyses of
P. Tsamir (*) : R. Ovodenko
Tel Aviv University, Tel Aviv, Israel
e-mail: pessia@post.tau.ac.il
P. Tsamir, R. Ovodenko
learners’ intuitive algorithmic and formal knowledge; Tall and Vinner’s (e.g., 1981) review of
concept image and concept definition; and Duval’s (e.g., 2006) investigations of the role of
representation and visualization in students’ (in)comprehension of mathematics.
2 Theoretical framework
In this section, we first survey the literature regarding: “What does research tell us about
students’ conceptions of inflection points?” Then, we attend to the question: “What are
possible sources of students’ mathematical errors?”
2.1 What does research tell us about students’ conceptions of inflection points?
In the literature, there are some indications of difficulties that students encounter when using
the notion of inflection points. For example, studies on students’ performances on connections between functions and their derivatives within realistic contexts reported that students
tend to err when identifying or when representing inflection points on graphs (e.g., Monk,
1992; Nemirovsky &amp; Rubin, 1992; Carlson, Jacobs, Coe, Larsen &amp; Hsu, 2002). Students
also tend to use fragments of phrases taken from earlier-learnt theorems, such as: “if the
second derivative equals zero [then] inflection point” even when solving problems in the
context of “dynamic real-world situation” (Carlson et al., 2002, p. 355). On this matter,
Nardi reported in her book: Amongst mathematicians: Teaching and learning mathematics
at university level that “there is the classic example from school mathematics: how the
second derivative being zero at a point implying the point being an inflection point” (Nardi,
2008, p. 66). An interesting, related piece of evidence was found in Mason’s (2001)
reflection on his past engagement (as an undergraduate) with the task: “Do y=x5 and y=x6
have points of inflection? How do you know?” Mason recalled being familiar with the
shapes of y=x5 and y=x6, thus knowing immediately which does and which does not have an
inflection point. Still, he clearly remembered being perplexed when reaching in both cases,
f ″ (x)=0 at x=0, and wondering why is it that one has an inflection and the other not? These
data indicate that even future mathematicians may experience (as undergraduates) intuitive
unease when encountering the insufficiency of f ″ (x)=0 for an inflection point.
Gomez and Carulla (2001) reported on students’ grasp of connections between the
location of inflection points and the location of the graph related to the axes. Students
claimed that, if an inflection point of y=f (x) is on the y-axis or “close enough” to it, then the
graph crosses that axis; if an inflection point is “not close, yet not too far from the y-axis,”
then the y-axis is an asymptote of the graph; if an inflection point is “far enough from the yaxis,” then the graph has other asymptotes; and if the inflection point “is far enough from xaxis,” then it does not cross that axis. Another line of research on students’ conceptions of
inflection points addressed issues related to the tangent at such points (e.g., Artigue, 1992;
Vinner, 1982; 1991; Tall, 1987). For instance, Vinner (1982) reported that early experiences
of the tangent of circles led learners to believe that “the tangent is a line that touches the
graph at one point and does not cross the graph” (see also Artigue, 1992; Tall, 1987).
In an early study, we examined university students’ conceptions of inflection points. We
came across a novel tendency to regard a “peak or bending point” (i.e., a point where the
graph keeps increasing or decreasing but dramatically changes the rate of change) as an
inflection point (Tsamir &amp; Ovodenko, 2004). We also found tendencies to regard f ′ (x0)=0
necessary for the existence of an inflection point at x=x0 (Ovodenko &amp; Tsamir, 2005); and
we observed that different erroneous conceptions of inflection points evolve when students
University students’ grasp of inflection points
address a variety of tasks. Thus, we instigated another study to examine university students’
conceptions when solving problems that offer rich opportunities to address inflection points.
We report here on the findings.
2.2 What are possible sources of students’ mathematical errors?
The analysis of students’ common errors calls for the use of related theoretical frameworks.
Usually, studies on students’ conceptions use one interpretational framework to shed light on
the data. We enriched our scope of analysis by using the perspectives offered by Fischbein
(e.g., 1987), Tall and Vinner (e.g., 1981), and Duval (e.g., 2006) that were widely used by
mathematics education researchers for analyzing students’ common errors and for examining possible related sources.
Fischbein (e.g., 1987, 1993b) claimed that students’ mathematical performances include
three basic aspects: the algorithmic aspect, i.e., knowledge of rules, processes, and ways to
apply them in a solution, and knowledge of “why” each of the steps in the algorithm is
correct; the formal aspect, i.e., knowledge of axioms, definitions, theorems, proofs, and
knowledge of how the mathematical realm works (e.g., consistency); and the intuitive aspect
that was characterized as immediate, confident, and obviously grasped as correct although it
is not necessarily so. Fischbein explained that, while neither formal knowledge nor algorithmic knowledge are spontaneously acquired, intuitive knowledge develops as an effect of
learners’ personal experience, independent of any systematic instruction. Sometimes, intuitive ideas hinder formal interpretations or algorithmic procedures and cause erroneous, rigid
algorithmic methods, which were labeled algorithmic models. For example, students’
tendencies to claim that (a+b)5 =a5 +b5 or sin(α+β)=sin α+sin β were interpreted as
evolving from the application of the distributive law (Fischbein, 1993a). Fischbein’s analysis
of students’ intuitive grasp of geometrical and graphs-related notions led him to coin the
term figural concepts, i.e., mental, spatial images, handled by geometrical or functions-based
reasoning. Figural concepts may become autonomous, free of formal control, and thus
erroneous (Fischbein, 1993a). Fischbein noted that a certain interpretation of a concept
may initially be useful in the teaching process due to its intuitive qualities and its local
concreteness. But, as a result of the primacy effect, this initial model may become rigidly
attached to the concept and generate obstacles to advanced interpretations of the concept.
Fischbein’s framework was widely used to analyze students’ mathematical conceptions of
various notions, such as functions, infinity, limit, and sets (e.g., Fischbein, 1987).
Two other researchers who examined learners’ grasp of mathematical notions are Tall and
Vinner (e.g., 1981) who coined the terms concept-image and concept-definition. Concept image
includes all the mental pictures and the properties that a person associates with the concept
name. When solving a certain task, specific aspects of one’s concept image are activated, the
evoked concept image. Concept definition is a term used to specify the concept in a way that is
accepted by the mathematical community, but learners often hold a personal concept definition
that may differ from the formal one. Moreover, the concept image which is frequently shaped
by some examples that do not fit the concept definition has a crucial impact on the reconstruction of the concept definition when the latter is called for (Vinner, 1990). Occasionally, one part
of the concept image becomes a potential conflict factor by implicitly conflicting with another
part of the concept image or with the concept definition. For instance, the concept of tangent is
usually introduced with reference to circles, implicitly insinuating that a tangent should only
meet the curve at one point and should not cross the curve (e.g., Vinner, 1991). This often
becomes part of the students’ tangent-image (generic tangent c.f. Tall, 1987) that may cause
problems later, for instance, at inflection points. Potential conflict factors contain the seeds of
P. Tsamir, R. Ovodenko
future conflict, but a key condition for one to actually face cognitive conflict is awareness. That
is, two incompatible images may obliviously coexist and be interchangeably used due to
compartmentalization in one’s mind (i.e., different ideas are placed in “separate drawers”).
However, when conflicting aspects are evoked simultaneously, they could cause an actual sense
of conflict or confusion, which can serve as a starting point in instruction. Tall and Vinner’s
framework proved to be useful in analyzing students’ conceptions of various mathematical
notions and specifically those related to advanced mathematics (e.g., limits, continuity, tangent:
Tall, 1987; Tall &amp; Vinner, 1981; Vinner, 1982).
Duval offered a different perspective that “representation and visualization are at the core
of understanding mathematics” (1999, p. 3). He explained that a major difficulty in mathematics comprehension is rooted in the nature of mathematical objects that can be accessed
only through signs while, in other sciences (e.g., biology), objects may also be accessed in a
direct manner. Duval analyzed representations of mathematical objects and processes and
suggested different routes of their mobilizations in learners’ minds (e.g., Duval, 2000, 2002;
see also an extensive discussion of Duval’s contribution in Hesselbart, 2007). He pointed to
three types of representation: (a) mental representations (individuals’ conceptions and
misconceptions about realistic, concrete objects); (b) computational representations (types
of information-codification used in mathematical algorithmic performances); and (c) semiotic representations (include signs, relationships, rules of production and transformation,
related with particular sign systems such as language, algebra, and graphs). A semiotic
representation provides an “organization of relations between representational units” and
since visualization allows an immediate and complete capture of any organization of
relations, “there is no understanding without visualization” (Duval, 1999, p. 13). Duval
emphasized the role of semiotic representations and of related transformational functions in
mathematics learning and called attention to essential differences between treatment, i.e., a
transformation within a single semiotic system and conversion, i.e., a transformation from
one semiotic system to another. For example, transforming x(x+1) into x2 +x within the
algebraic system is treatment, while transforming x(x+1) into a graph of the parabola, i.e.,
from the algebraic system into the Cartesian system, is conversion. Duval (e.g., 2006)
stressed that conversions are crucial in mathematical activities and that students’ difficulties
with mathematical reasoning lie in the cognitive complexity of conversions that entail
recognition of a mathematical object in different representations and discrimination between
“what is mathematically relevant and what is not,” when examining a mathematical object.
Another major source of difficulties is rooted in the direction of conversion. “When roles of
source register and target register are inverted within a semiotic representation conversion
task, the problem is radically changed for students. It can be obvious in one case, while in the
inverted task most students systematically fail” (ibid., p. 122). Furthermore, conversion of
representations requires the cognitive dissociation of the represented object and the content
of the specific representation in which the object was first introduced; on the other hand,
there is a cognitive impossibility of dissociation of any semiotic representation content and
the first representation of the object, because the only access to mathematical objects is
semiotic. Consequently, students erroneously perceive two representations of the same
object as being two mathematical objects, i.e., the registers of the representations remain
fragmented and compartmentalized. Duval’s framework has been valuable in analyzing
students’ engagement in a wide range of mathematical topics such as geometry, functions,
algebra, vectors, and number systems (e.g., Duval, 1999, 2000, 2006).
We use the ideas and the terminology offered by Fischbein, by Tall and Vinner, and by
Duval to examine university students’ conceptions of inflection points, by focusing on the
questions: What are students’ common errors, and what are possible sources of these errors?
University students’ grasp of inflection points
3 Methodology
3.1 Participants
We report on two studies, investigating two groups of university students. In one study, 53
students were asked to solve tasks 1–3; in the second study, 52 students were asked to solve
tasks 4–6. All participants had studied mathematics at mathematics faculties or at
mathematics-oriented faculties, i.e., computer science, computer engineering, and electronic
engineering. All had successfully completed at least two calculus courses, two linear algebra
courses, and a course of differential equations. The participants’ ages ranged between 25 and
35 years, and all expressed interest and enthusiasm in their studies.
3.2 Tools and procedure
The research tools were questionnaires and individual oral interviews. We present six tasks
from the questionnaires that included reference to inflection points.
True or false?
f: R ⇒ R is a continuous, differentiable function.
If A(x0,f (x0)) is an inflection point, then f ′ (x0)=0.
True/false, prove:
Statement 2: f: R ⇒ R is a continuous, (at least twice) differentiable function.
If f ″ (x0)=0, then A(x0, f (x0)) is an inflection point.
True/false, prove:
Figure 1 presents five graphs. On each graph mark Zi all points of intersection
with the axes; Xi maximum-points; Ni minimum-points; and Pi inflection points.
What is an inflection point?
Investigate the function f &eth;x&THORN; &frac14; 14 x4 x3 . Are there:
Statement 1:
(a) Points of intersection with the axes? Yes/no, if “yes” what are the points? Explain.
(b) Maximum/minimum points? Yes/no; if “yes,” what are the points? Explain.
(c) Inflection points? Yes/no; if “yes,” what are the points? Explain.
(d) Asymptotes? Yes/no; if “yes,” what are they? Explain.
Task 5 Investigate f ′ (x)
Note: in the following task, f ′ (x) is given, but the questions are about f (x).
f 0 &eth;x&THORN; &frac14; 15x2 5x3 . Does f (x) have:
Fig. 1 The graphs presented in Task 2
P. Tsamir, R. Ovodenko
(a) Maximum/minimum points? Yes/no; if “yes,” find the related Xs. Explain.
(b) Inflection points? Yes/no, if “yes,” find the related Xs. Explain.
Task 6: Investigate the graph of f ′ (x)
Note: in the following task, the graph of f ′ (x) is given, but the questions are about f (x).
Figure 2 presents the graph of f ′ (x). Does f (x) have:
(a) Maximum/minimum points? Yes/no; if “yes,” find the related Xs. Explain.
(b) Inflection points? Yes/no, if “yes,” find the related Xs. Explain.
The tasks were formulated with a number of underlying deliberations (Fig. 3). First, since
the concept of inflection point has both graphical and non-graphical aspects which play
significant roles in students’ figural and conceptual knowledge and influence their concept
images (see, for instance, Duval, 2006; Fischbein, 1993a; Vinner &amp; Dreyfus, 1989), the
algebraic (tasks 4, 5). The solutions could be formulated either by treatment within the given
representations or through conversion.
We learnt from the teachers of these courses that, most commonly, students analyzed
algebraic expressions of functions (task 4) and seldom analyzed algebraic expressions of the
derivative (task 5). In the case of the graphic representation, students analyzed given graphs
quite rarely (task 2) and even more rarely analyzed graphs of the derivative (task 6).
Students’ modest experience with such tasks and the impossibility of applying routine
algorithms in the related solutions led us to believe that students’ knowledge might be
challenged, especially in task 6. Finally, tasks 1 and 3 were in a way familiar to students
because definitions and theorems were customarily presented in their classes. However,
students were rarely asked to determine whether a statement is valid (task 1).
The questionnaires offered a number of opportunities to examine students’ tendencies to
erroneously view (a) f ′ (x)=0 as necessary for inflection (in task 1 judging statement 1 as being
true, in tasks 2 and 6 identifying “only horizontal inflection points,” in task 3 adding f ′=0 to
their definitions, and in tasks 4 and 5 adding to the algorithm f ′ (x)=0 as a necessary condition);
and (b) f ″ (x0)=0 as sufficient for inflection (in task 1 judging statement 2 as being true; in task
6 identifying “only f ″=0 points,” in task 3 not going beyond the condition f ″ (x)=0 in their
definitions, and in tasks 4 and 5 stopping the algorithm after the stage of f ″ (x)=0). Moreover, in
the tasks presented in the algebraic and in the graphic representations the inflection points
Fig. 2 The graph of y=f ′ (x)
University students’ grasp of inflection points
No 1 / No 3
Instruction
Verbal
Judge and Prove / Define
No 4 / No 5
No 2 / No 6
Symbolic - Algebraic
Investigate Expression
Graphical
Investigate Graph
The Solution
Possible Representation
*Verbal / or
*Symbolic / and
*Symbolic / or
Verbal / or
Graphic / or
a combination
*Graphic / and
Symbolic / or
Graphic / or
Verbal / or
a combination
a combination
Connecting
Inflection point
f '= 0, f &quot;= 0 / open
f (x) / f ' (x)
f (x) / f ' (x)
Fig. 3 Task analysis * the most expected representations
task 5, and task 6), and a vertical point (task 2). The different perspectives were devised to
provide students with rich opportunities to evoke images of inflection points (see previous
section, Tall &amp; Vinner, 1981).
Based on the analysis of their written solutions, 15 participants were invited to
individual follow-up interviews. We interviewed students whose written solution we
found to be interesting, unclear, or puzzling. During their interviews, students were
shown their original solutions and were asked to provide an elaborate explanation of
their written ideas. The interviews lasted 30–45 min and were audio-taped and
transcribed.
4 Discussion of the results
In this section, we discuss our data about students’ conceptions of inflection points and
about proof-related difficulties that students encountered when performing on the inflectionpoint tasks. We use theoretical lenses to discuss the identified difficulties.
4.1 Students’ grasp of inflection points
Our data indicate four main erroneous images of inflection points: (1) slope zero, f ′ (x)=0
is necessary for an inflection point, (2) non-zero slope, f ′ (x)≠0 is necessary for an
inflection point, (3) f ″ (x)=0 is sufficient for an inflection point, and (4) peak points,
“where the graph bends” are inflection points. We discuss each of these concept images,
first by reporting on relevant findings, and then by analyzing the data presented in light of
Fischbein’s, Tall and Vinner’s, and Duval’s theoretical frameworks.
1. Slope zero, f ′ (x)=0 is necessary for an inflection point: This concept image is evident in
students’ solutions to all six tasks. In task 1: True or False? about 60 % of the students
incorrectly claimed that Statement 1, “if P is an inflection point, then f ′ (P)=0” is true
(Table 1).
Their explanations usually (45 %) related to the definition of inflection points—“the f ′ (x)=0
condition is in the definition of an inflection point” or “f ′ (x)=0 and f ″ (x)=0 (and perhaps
additional conditions) are the conditions for an inflection point.”
P. Tsamir, R. Ovodenko
Table 1 Distribution of students’
reactions to the statements (in %)
Judgment
Justification
Falsea
N=53
N=53
38
55
30
13
Counter-example
8
42
59
40
“It’s the definition …”
Algorithmic considerations
45
4
17
23
Irrelevant/no justification
10
–
3
5
Correct judgment
f ″=0⇒Inflection
There is another condition
True
a
Inflection
point ⇒ f ′=0
In task 2: Investigate the Graphs students were asked to mark on given graphs points of
intersection with the axes (Zi), extreme points (Xi–Max and Ni–Min), and inflection points
(Pi). Students who marked Zi, Xi, or Ni points, but no Pi points, were regarded as students
who identified no inflection points. Commonly, students correctly marked the horizontal
inflection points P3 on Graph 2 (95 %) and P10 on Graph 5 (75 %); the percentages of
identifications of the other inflection points were much lower (0 to 17 % for each of the
points, see Table 2).
To get an overall perspective of students’ solutions to task 2, we created profiles of their
identifications of inflection points in all five graphs (Table 3). We found three main profiles
of solutions: (a) partial, (b) correct–incorrect mix, and (c) incorrect. No student correctly
identified all inflection points on the five graphs. Students’ tendencies to identify only
horizontal inflection points were evident in the partial selection of 19 % of the students
who marked only the horizontal points P3, P10.
In Task 3: What is an inflection point? Students’ tendencies (15 %) to regard f ′=0 as “a
must” at inflection points were manifested in their slope-zero definitions that included f ′ (x)=
0 or slope-zero consideration (Table 4): “A point where f ′ (x)=0 and the graph keeps
increasing (decreasing) before the point and after it”; or “an inflection is a point where f ″
and f ′ are zero”.
In reaction to Task 4: Investigate the function f (x)=1/4 x4 −x3, half of the students used
algorithms that erroneously included f ′(0)=0 as a critical attribute and reached a single
inflection point at x=0 (Table 5). Most of them (40 %) examined f ′ (x)=0, f ″ (x)=0, f ‴ (x)≠
0, and wrote (Fig. 4):
Table 2 Distribution of students’ identifications of inflection point in each graph (N=52)
Graph 1
none P1 P2 all
83 17 17 17
Graph 2
Graph 3
none P3 P4 P5 all
none P6 P7 all
4
95 12
-- --
42
17 12 8
Graph 4
Graph 5
none P8
none P10
35
17
--
75
University students’ grasp of inflection points
Table 3 Distribution of students’
identifications of inflection point
in all five graphs (%)
Profile
A partial identification
Horizontal inflection points (P3/P10)
Inflection points of different types (except for P4/P5)
A mix correct and incorrect identificationa
a
Incorrect identification—where
the graph bends
N=52
27
19
8
70
Horizontal inflection points+peak curve
41
Inflection points of different types+peak curve
29
Only incorrect identificationa
3
f &eth;x&THORN; &frac14; 14 x4 x3 ; f 0 &eth;x&THORN; &frac14; x3 3x2 ;
f 0 &eth;x&THORN; &frac14; 0 &frac14;&frac14;&gt; x &frac14; 0; x &frac14; 3
f &micro;&eth;x&THORN; &frac14; 3x2 6x &frac14;&frac14;&gt; f &micro;&eth;3&THORN; &gt; 0 &frac14;&frac14;&gt; A 3; 6 34 Minimum; f &micro;&eth;0&THORN; &frac14; 0
f &Oslash;&eth;x&THORN; &frac14; 6x 6; for x &frac14; 0; f &Oslash;&eth;x&THORN; 6&frac14;0 &frac14;&frac14;&gt; B&eth;0; 0&THORN; inflection point
The others (10 %) examined f 0 &eth;x&THORN; &frac14; 0 : &micro;f 0 &eth;x&THORN; &frac14; x3 3x2 &frac14; 0 &frac14;&frac14;&gt; x &frac14; 0; x &frac14; 3&micro;:
Then they used a table of signs (Fig. 5) and concluded that (0:0) is an inflection point.
Many students (42 %) based their solutions to Task 5, Investigate f ′ (x)=15x2 −5x3, on a
wrong assumption that f′ (P)=0 is a critical attribute. They incorrectly reached only the result
of x=0, either by examining f ′ (x)=0 and a table of signs for f ′ (x) (6 %), or by solving f ′ (x)=
0, f ″ (x)=0, f ‴ (x)≠0 (36 %):
f 0 &eth;x&THORN; &frac14; 15x2 5x3 ; f 0 &eth;x&THORN; &frac14; 0 &frac14;&gt; x &frac14; 0; x &frac14; 3
f &micro;&eth;x&THORN; &frac14; 30x 15x2 &frac14;&gt; f &micro;&eth;3&THORN; &lt; 0 &frac14;&gt; at x &frac14; 3 maximum point; f &micro;&eth;0&THORN; &frac14; 0
f &Oslash;&eth;x&THORN; &frac14; 3030x; f &Oslash;&eth;0&THORN; &frac14; 30 6&frac14; 0 &frac14;&gt; at x &frac14; 0 inflection point
Finally, in reactions to Task 6, Investigate the Graph of f ′ (x), most participants (62 %)
incorrectly found an inflection point only at x=0, exhibiting an erroneous assumption that f ′
(x)=0 is a must. Many (35 %) examined f ′ (x)=0 and f ″ (x)=0: “We see that at x=0, f ′ (x)=0,
that is, the slope of the tangent of y=f ′ (x) at x=0 is zero, so, f ″ (0)=0==&gt;at x=0 there is an
inflection point.” The others (27 %) explained that f ′ (0)=0 and in the neighborhood of x=0
f ′ (x) is positive—“f ′ (0)=0 and before and after x=0 f ′ is positive, so f increases before and
after x=0, thus x=0 is an inflection point.”
Table 4 Distribution of students’
solutions to task 3: What is an
inflection point? (%)
What is an inflection point?
N=52
Definition: correct or non-minimal
68
Slope zero definition
15
f ′ (x)=0/slope zero and increase–increase
9
Convex–concave and slope zero
2
f ′ (x)=0 and f ″(x)=0
Insufficient (missing) definition
f ″ (x)=0
Increase–increase
4
10
4
6
7
P. Tsamir, R. Ovodenko
Table 5 Distributions of students’ solutions to tasks 4, 5,
and 6 (%)
Result
Algorithm
f (x)=1/4 x4−x3
f ′(x)=15x2 −5x3
Graph
of f ′
N=52
N=52
N=52
50
48
27
f ″=0; f ″≠0
12
12
–
f ″=0
38
36
27
50
42
62
f ′=0; f ″=0; f ″≠0
40
36
–
f ′=0; table of signs
10
6
27
f ′=0; f ″=0
–
–
35
–
10
11
A (0; 0) B (2; −4)a/
x=0, x=2a
A (0: 0)/x=0
a
Correct solution
Other
In sum, many participants expressed f ′ (x)=0 inflection point images that were evident in
the different types of tasks and in the three (verbal, graphic, and algebraic) representations
(Tall &amp; Vinner, 1981). For example, participants exhibited slope-zero figural concepts when
marking only horizontal inflection points in their reactions to all five graphs (Fischbein,
1987) and slope-zero definitions, in response to “what is an inflection point?” when
including the condition f ′ (x)=0 or slope zero in their personal definitions (Tall &amp; Vinner,
1981). In tasks 4, 5, and 6, 50 %, 42 %, and 62 % of the students, respectively, found an
inflection point only at x=0, since f ′ (x)=0 was used as a critical step in their algorithmic
models (Fischbein &amp; Barash, 1993, Fischbein, 1993b).
Many students explained their f ′ (x)=0 solutions by highlighting that “this is how I
calculate (find) an inflection point when I investigate a function.” Here, like in many other
cases, students learn to recognize concepts “by experience and usage in appropriate contexts” (Tall &amp; Vinner, 1981, p. 151; authors’ emphasis). According to the Israeli mathematics
curriculum for secondary schools, when students start investigating functions, they solve f ′
(x)=0 to find possible Xs of extreme points. In these routine investigations, they accidentally
encounter cases where f ′ (x)=0, but there is no extreme point “because the function keeps
increasing (or decreasing) in the interval that includes this point.” As Tall and Vinner (ibid)
explained, “usually in this process the concept is given a symbol or name which enables it to be
communicated” and differentiated from the other, related concepts. Indeed, in cases of f ′ (x)=0,
yet non-extreme points, students are first guided to label the points—inflection points because
these are inflection points and for purposes of communication and differentiation from extreme
points that are discussed at that time. These processes are students’ prime, inflection-points-
Fig. 4 Points marked by the students as inflection points (correct Pi; incorrect Ti)
University students’ grasp of inflection points
x
x&lt;0
x=0
0&lt;x&lt;3
x=3
x&gt;3
f '(x)
negative
zero
negative
zero
positive
f (x)
decreases
Inflection-point
decreases
minimum-point
increases
Fig. 5 The table of signs
related, mathematical experiences, which intuitively shape their images of the notion, and set a
cornerstone for a robust primacy-effect that would be expressed in future experiences with
inflection points (Fischbein, 1987). In other words, students are first introduced to inflection
points arbitrarily, while implementing rules of calculus to search for extreme points during
treatment transformations on algebraic representations of functions, often concluding by conversions, transforming the symbolically represented solutions to a graphic Cartesian representation (e.g., Duval, 2002). Consequently, over a substantial period of time, only horizontal inflection
points are repeatedly encountered and labeled in students’ primary experiences that are approved
and authorize in class. Thus, students’ recognition of inflection points, both in symbolic–algebraic
representations and in Cartesian–graphic representations, naturally becomes limited to horizontal
inflection points. In future studies, it becomes quite demanding (or impossible) for students to
cognitively dissociate the content and the specific representation in which inflection was first
introduced and used in their early schoolwork (Duval, 2006).
2. Non-zero slope, f ′ (x)≠0 is necessary for an inflection point—In reaction to Statement 1
if A is an inflection point then f ′ (xA)=0 in Task 1, several students (15 %) exhibited
awareness of their primary tendencies to view f ′ (x)=0 as “a must” and knew that this
requirement is wrong. While they correctly answered that the statement is false; they
incorrectly added the condition that f ′(x) should not be zero. In his interview, one of them
explained: “I used to believe that f ′ (x) is always zero at inflection points, but I realized it
isn’t. Quite the opposite, like in cos(x), f ′ (x) should not be zero”. This student made an
over-correction, from f ′ (x) must be zero (error) to f ′ (x) must be non-zero (error).
In an attempt to correct their initial error, students excluded the condition f ′ (x)=0 from
the definition of inflection points, by shifting to a reverse, erroneous personal definition that
included f ′ (x)≠0 as “a must.” This error might be rooted in intuitive ideas that interfere with
students’ formal knowledge, incorrectly leading them to believe that, logically, the complementary set of “A—always” (f ′ (x)=0 is always true at inflection points), is “A—never” (f ′
(x)=0 is never true at inflection points); or “not A—always” (f ′ (x)≠0 is always true at
inflection points), rather than “A not-always” (f ′ (x) is not always zero at inflection points,
sometimes it is and sometimes not) (Fischbein, 1987, 1993a). That is to say that this error
has roots in the specific content of inflection points, by reflecting on such points and
doubting whether “f ′ (x)=0 is a necessary condition for the existence of such points.” At
first, the notion of inflection points is placed at the heart of the investigation. However, in
our study, following the participants’ conclusion that the condition is not a necessity for
inflection point, we surmise that their focus-of-reasoning might have been reallocated to the
wide mathematical realm of logic, proofs, and refutations. Nardi (2008, p. 70) reported on a
university student who was “confusing not always with always not…”, and this might be the
case here too.
3. f ″ (x)=0 is sufficient for an inflection point—Our data indicate this belief mostly in
students’ solutions to Tasks 1, 4, 5, and 6. In reaction to Task 1, True or False? 40 % of
the students incorrectly claimed that Statement 2 (f ″ (x)=0 ⇒ inflection point) is true
P. Tsamir, R. Ovodenko
(Table 1). Many (17 %) justified that “That’s the definition: P is an inflection point if and
only if f ″ (XP)=0”. Many others (23%) provided algorithmic considerations, mentioning
solutions to investigate-a-function and stopping after solving f ″ (x)=0, e.g., “We find
inflection points when looking for extreme points. At extreme points f ′ (x)=0 and f ″ (x)≠0,
if f ″ (x)=0, it isn’t an extreme point, it’s an inflection point.”
In Task 4, Investigate the function, f (x)=1/4 x4−x3 and in Task 5: Investigate f ′ (x)=15x2 −
5x , about 40 % of the students used in their algorithms f ″ (x)=0 considerations as sufficient for
finding inflection points (Table 5). In Task 4, half of the students reached the correct result, A(0;
0) and B(2; −4) are inflection points, but 38 % insufficient examined only f ″ (x)=0. They wrote:
3
f &eth;x&THORN; &frac14; 1=4 x4 x3 ; f 0 &eth;x&THORN; &frac14; x3 3x2 ; f &micro;&eth;x&THORN; &frac14; 3x2 6x
f &micro;&eth;x&THORN; &frac14; 0 &frac14;&frac14;&gt; x &frac14; 0; x &frac14; 2 &frac14;&frac14;&gt; A&eth;0; 0&THORN;; B&eth;2; 4&THORN;
Similarly, in Task 5, about half of the participants correctly reached inflection points at x=
0 and at x=2, but 36 % of the students used an incorrect algorithm, examining only f ″=0:
If f 0 &eth;x&THORN; &frac14; 15x2 5x3 then f &micro;&eth;x&THORN; &frac14; 30x 15x2
f &micro;&eth;x&THORN; &frac14; 0 &frac14;&gt; 30x 15x2 &frac14; 0 &frac14;&gt; x &frac14; 0; x &frac14; 2; Inflection points are at x &frac14; 0; x &frac14; 2
In Task 6, Investigate the Graph of f ′(x), students (27 %), who correctly found x=0 and x=2
as the Xs of inflection points did not go beyond solving f ″ (x)=0 (Table 5), for example:
At x &frac14; 0 and at x &frac14; 2 the function y &frac14; f 0 &eth;x&THORN;has minimum and maximum
So; y0 &frac14; 0; &frac14;&frac14;&gt; f 00 &eth;x&THORN; &frac14; 0; &frac14;&frac14;&gt; x &frac14; 0 and x &frac14; 2 are inflection points
Many others (35 %) who found only x=0 as a solution, using the f ′ (x)=0 and f ″ (x)=0
algorithm, stopped their explanations after finding f ″ (x)=0, indicating their grasp of the
sufficiency of f ″ (x)=0 for inflection points, for example, “At x=0, the slope of the tangent
of y=f ′(x) is zero, meaning that, f ″ (0)=0, thus, x=0 is an inflection point.” So, in task 6,
62 % of the students exhibited this concept image.
Generally, students exhibited three f ″=0 related algorithmic models for finding, and for
defining inflection points (Fischbein &amp; Barash, 1993; Fischbein, 1993a): (a) solve f ″ (x)=0,
it is the only condition needed for inflection points; (b) solve f ′ (x)=0, then, for these Xs,
solve f ″ (x)=0 to find inflection points. Clearly, these two algorithmic models are problematic. They do not necessarily lead to inflection points. For example, f (x)=x4 has both f ′ (0)=
0 and f ″ (0)=0, but there is no inflection point at zero (see also Mason, 2001). Then, several
students presented an erroneously revised grasp of inflection points: (c) calculate the Xs of
points that fulfill both conditions: f ″=0 and f ′≠0. The three algorithmic models that
erroneously included f ″ (x)=0 as sufficient for inflection points, intuitively served as
algorithmic bases for students’ concept images of the notion (see also, Carlson et al.,
2002; Fischbein, 1993b; Tall &amp; Vinner, 1981).
4. Peak points: points where the graph bends—We found evidence of students’ tendencies
to view inflection points at peak points “where the graphs bend,” in their answers to the
graphic representation of f (x) in Task 2. Students’ reactions to Task 2, Investigate the
Graphs, included three types of solutions: (a) correct (Pi); (b) incorrect (always where
the graph bends—Ti); (c) no identification. Figure 4 illustrates students’ correct (Pi) and
incorrect (Ti) solutions.
Many participants (73 %) marked, at least once, an inflection point at a peak-point.
Usually (70 %), they marked a correct–incorrect mix of Pi and Ti. Others (3 %) provided
University students’ grasp of inflection points
only incorrect identifications of inflection points, exclusively where the graph bends
(Table 3). One might expect students majoring in mathematics at university level to draw
on the definition and to convert the data to a graphical representation and to visual
considerations such as, “a point where the graph shifts from concave up to concave down
(or vice versa)” or to examine points (as a first step) “where the tangent intersects the graph.”
However, students did not justify these choices of inflection points by referring to a
definition (see also Nardi, 2008). In their interviews, students provided two types of
explanations for T being an inflection point: mathematically based explanations and
reality-based explanations.
The mathematical explanations were, for example, “at this point the graph keeps increasing, yet at a different pace” or “the graph keeps increasing, but the slope changes dramatically.” These students examined the graphic representation and converted their solution to
natural language while addressing the mathematical realm (Duval, 2006). Such explanations
are commonly given in Israeli mathematics secondary-school classes, at the early stages of
investigations of functions, with good intentions to encourage students’ recognition of
extreme points (where the graph does not continue increasing/decreasing) and students’
discrimination between what is mathematically relevant and what is not when addressing
extreme points in different representations. At these early stages, the description (e.g., “at
that point the graph keeps increasing, but the slope changes dramatically”) and the labeling
of inflection points is merely a superfluous byproduct in the studies of extreme points, but
with time this knowledge becomes a critical attribute of inflection points (Duval, 2006).
Another interpretation of students’ confused ideas may be rooted in their vast mathematical engagement with constructing graphs (converting a verbal representation or a symbolic
representation to a graphic representation) and their scarce experience with analyzing given
graphs (converting a graphic representation to a verbal representation). Commonly, when
working on inverted task, most students systematically fail (Duval, 1999).
The reality-based explanations were explanations embedded in daily context, such as, “if
I drive north on a curved road, the point where I must turn the wheel, but still keep north, is
an inflection point”; or “when a plane takes off, it first goes up moderately, and at a certain
point, at an inflection point, suddenly it changes the slope and keeps going up, yet in a
steeper manner”; or “it’s like climbing a mountain that at the bottom has a gentle upward
slope, and at a distinct point becomes a challenging, bold upward slope. That point is an
inflection point.” Duval stated that “Cartesian graphs do not work visually for most students
except for giving the na&iuml;ve holistic information: the line goes up or down, like a mountain
road” (Duval, 1999, p. 16). That is, one source of difficulties might be found in the nature of
visualization that “consists in grasping directly the whole configuration of relations and in
discriminating what is relevant in it; most frequently, students go no further than to a local
apprehension and do not see the relevant, global organization but an iconic representation”
(ibid., p. 14). Consequently, what might have been expected to be a most straightforward
visual representation was actually most problematic for students. So problematic that
students’ explanations and reasoning intuitively “crossed the conventional borders of the
mathematical realm” to irrelevant realistic contexts.
The graphic representation was the only one that evoked “peak points” concept images
(the way students envisioned the points), primary intuitions (the way students primarily
encountered such points in realistic settings), and figural concepts (the way they grasped the
graphical presentation of the points) of inflection points in the solutions of advanced
mathematics learners (Fischbein, 1987; Tall &amp; Vinner, 1981; Tsamir &amp; Ovodenko, 2004).
In daily communication in Hebrew, the realistic explanations that were commonly given to
inflection points are consistent with what we commonly label ‘inflection points’ in daily
P. Tsamir, R. Ovodenko
context. Thus, it is possibly as a result of the primacy effect that this initial model became
rigidly attached to the concept (Fischbein, 1987), and due to processes of compartmentalization
(e.g., Duval, 2006; Vinner, 1990), incompatible images (e.g., inflection points are horizontal,
and inflection points are at peak-points) were interchangeably used. Vinner (1991) stated that
particularly with such students, “one of the goals of teaching mathematics should be changing
the thought habits from the everyday life mode to the technical mode” (p. 80).
Concisely, our analyses of students’ common errors in this study yielded that students’
intuitive knowledge and concept images (as expressed, for instance, in their solutions to
Tasks 1, 2, and 3), and their algorithmic models (as expressed, for instance, in their solutions
to Tasks 4, 5) of inflection points resulted from their past experiences with the concept in
mathematics or in realistic occurrences. Students’ solution methods and their justifications
addressed the calculus-outline in the Israeli curriculum for high school student (e.g., first,
teach extreme points in graphs investigation, afterwards, inflection points), and realistic–
linguistic considerations (in Hebrew the realistic peak-points that were described in reaction
to Task 2 are labeled inflection points).
Nardi (2008, p. 41) found that university students frequently experienced major tensions
between the familiar (concrete, numerical) and the unfamiliar (rigorous, abstract) and
between the general and the particular, when trying to employ formal, mathematical
reasoning. Our data indicate matching phenomena. The tension between the familiar and
unfamiliar revolved around the tension between students’ intuitive, familiar grasp of inflection points either being horizontal (familiar from class) or at peak points on graphs (familiar
from reality), and from students’ (usually non- or mal) employment of definitions. The
tension between the general and the particular was found in students’ overgeneralization of
the necessity that inflection points be (or not be) horizontal.
4.2 Students’ proofs—validations and refutations
In task 1, students had to refute two “for all” statements, for instance, by providing related
counterexamples, with which they were familiar from their mathematics classes (e.g., to refute
statement 1, f ′ (x)=0 as a necessary condition: f (x)=sin(x); to refute statement 2, f ″ (x)=0 as a
sufficient condition: f (x)=x4). Still, only less than half of the participants correctly answered
that statement 1 is false, and 55 % of the participants correctly judged that statement 2 is false.
Moreover, only 8 % of the students correctly refuted statement 1 by a counter example, and
42 % presented counter examples to refute the statement 2 (Table 1).
In both cases, a substantial number of students based their correct “false” judgments on
incorrect “there is another condition” considerations. In reaction to statement 1, 15 % of the
students erroneously added the “f ′(x) should not be zero” condition, confusing the logical
term not necessarily zero with necessarily not zero (see also, Nard, 2008). In reaction to
statement 2, 13 % of the students added the unnecessary condition f ′ (x0)=0.
Many students wrongly accepted the statement 1 (59 %) and statement 2 (40 %) as true
(Table 1). One may wonder why? Are there possible sources for such difficulties that go
beyond the specific content of “inflection”? We would like to address these erroneous
solutions by relating to wide perspectives of logic and proof. Perhaps, students, who are
accustomed to be presented in class with (valid) theorems, or rarely to be informed of the
falsity of a statement, but are never asked to decide themselves true-or-false, might have had
similar expectations (see, Nardi, 2008). Possibly, their implicit assumptions here were that,
as usual, they are asked to address theorems, and thus, they did not even try to look for a
counterexample; they were only trying to provide us “validating” justifications to satisfy our
request for explanations.
University students’ grasp of inflection points
Another reason might be embedded in students’ poor “proving skills” and their lack of
strategic knowledge of proofs and refutations (i.e., skills of identification of the available range
of counterexamples or theorems, knowledge of ways to select them appropriately, and to draw
on them efficiently when proving). Weber (2001) reported on a study where PhD students had
might be that such skills ripen with students’ mathematical, academic experience over time.
Statement 2, regarding the sufficiency of f ″ (x)=0, is false, yet the converse statement is
true for functions which are differentiable at least twice on the range. It was found that when
addressing statements like statement 2, university students tended to be “unable to distinguish between a main and a subordinate clause,” for example, confusing statements like “if
you are in the running shower then you are wet” with the converse statement, so that “if you
are wet, it doesn’t follow that you are in a shower” (Nardi, 2008, p. 59). On the other hand,
students might have been extremely, yet mistakenly, taken by the mathematical content of
the statement, so that they were not open to engage in the logical aspects of the statements
Moreover, students “proved” their mistaken “true” judgments to both statements in two
ways: (a) by stating that this condition is “part of the definition” of inflection points and (b)
erroneous proving methods carried a sense of generality, and neither had the form of
empirical proofs. That is, we found no tendencies to base the “proofs” of “the statement is
true” answers, on specific examples. These findings indicate that, unlike widely reported,
previous data that pointed to extensive tendencies of students of various ages to provide
specific examples as proof of for-all statements (e.g., Balacheff, 1987; Bell, 1976; Harel &amp;
Sowder, 2007), the participants of this study exhibited no such tendencies. They provided
erroneous, seemingly general, pseudo-cover proofs (e.g., Vinner, 1997).
5 Summing up and looking ahead
This study listed four common, inflection-point related errors that were identified in
students’ responses to our questionnaire and additional difficulties, related to students’
proofs and refutations.
Our investigation and the analysis of the data in light of Fischbein’s, Tall and Vinner’s, and
Duval’s frameworks regarding students’ comprehension, intuitions, and images indicate that
these errors and difficulties were not light cases of mere confusion but deep, “logical” problems,
rooted, among others, in the nature of mathematics, in mathematics knowledge acquisition, and
in learners’ primary engagement with the notion in mathematical and daily contexts. Clearly,
one may choose other theoretical models to shed light on the findings. Another approach,
suggested by Vinner (1997) himself, indicates that students’ erroneous solutions may be rooted
in pseudo-conceptual or pseudo-analytical thought processes, which are “based on the belief
that a certain act will lead to an answer that will be accepted …or will impress society.” That is,
rather than dealing with students’ “belief that statement X is true [we deal with their belief] that
statement X will be credited by person Y who is supposed to evaluate it” (ibid., p. 115). The
desire to get some credit in a certain social setting may guide students to apply fuzzy memory
and superficial generalizations, in the way that they may have used the conditions f ′ (x)=0, or f ′
(x)≠0″, and the f ″(0)=0 for inflection points.
We found that university students have erroneous concept images of inflection points.
Two of the images were that f ′ (x)=0 is necessary and that f ″ (x)=0 is sufficient for
inflection points. When we shared these data with colleagues, their reactions were that these
P. Tsamir, R. Ovodenko
findings are self-evident. It seems that, while many mathematics educators and high school
teachers have repeatedly yet randomly encountered such errors, in this study, we provided for the
first time, to the best of our knowledge, extensive research-based evidence, systematically
collecting these data from students’ reactions to various representations of tasks and their clear
declarations about these beliefs. Thus, we regard these findings as valuable research-based
evidence for researchers and for teachers. The two other common errors that we found in this
study, i.e., students’ tendencies to regard f ′ (x)≠0 as necessary, or “peak points” on graphs as
inflection points were neither reported in the literature nor familiar to our colleagues. Again, the
data seem to extend the knowledge base regarding students’ grasp of inflection points. However,
more research is needed to study students’ intuitions, concept images, visualization, definitions,
proofs, and comprehension of conversions from certain semiotic representations to other.
Moreover, when considering instructional implications,
One must remember that a concept is not acquired in one step. Several stages precede
the complete acquisition and mastery of a complex concept. In these intermediate
stages, some peculiar behaviors are likely to occur. Several cognitive schemes, some
even conflicting with each other, may act in the same person in different situations that
are closely related in time… knowledge of these particular cognitive schemes may
make the teacher more sensitive to students reactions’ and thus improve communication. (Vinner &amp; Dreyfus, 1989, p. 365)
A first step in student-sensitive instruction is an examination of “what do the students that I am
going to teach know”? One suggestion would be to present students who are about to learn the
notion inflection points with a questionnaire, for instance, like the one we used here to get
acquainted with their initial views and common errors. This study provides teachers with ideas
regarding errors that should be expected. Since classes vary, and a certain error might be more
evident in one class and less in another, a teaching plan should take these data into consideration.
Fischbein (1987) and Vinner (1991; 1997) stated that it is important to promote students’
(especially those majoring in mathematics) awareness of their mathematical, intuitive, and
pseudo-mathematical ways of reasoning. “It seems that the formation of control mechanisms in
a person’s mind might help a lot … this can be done by encouraging the person to reflective
thinking” (Vinner, 1997, p. 127). Conflicts between the concept images and the formal definition
should be identified and thoroughly discussed, and “if the students are candidates for advanced
mathematics then, not only that definitions should be given and discussed, the students should be
trained to use them as an ultimate criterion in mathematics tasks” (Vinner, 1991, pp. 80). Taking
into account these recommendations and our findings regarding students’ tendencies to grasp
f ′ (x)=0, or f ′ (x)≠0 as necessary conditions, we suggest tasks like the following:
A True or false?
f: R ⇒ R is a continuous, differentiable function.
Statement 1: If A(x0, f (x0)) is an inflection point, then f ′ (x0)=0. True/false; prove.
Statement 2: If A(x0, f (x0)) is an inflection point, then f ′ (x0)≠0. True/false; prove.
B Investigate the functions
a.
For each function, indicate: Are there inflection points? If yes, what are they?
For each inflection point, is f ′ (x)=0 or f ′ (x)≠0?
&frac12;a1 f &eth;x&THORN; &frac14; sin x; &frac12;a2 f &eth;x&THORN; &frac14;
1 4
x x3 &frac12;a3 f &eth;x&THORN; &frac14; 12x5
4
University students’ grasp of inflection points
C Reexamination and reflection
Look at statement 1 and statement 2 once more; did you change your solutions? Why?
Duval claimed that “the use of visualization requires a specific training, specific to
visualize each register, [and] the student can succeed in constructing a graph and being
unable to look at the final configurations other than as iconic representations” (Duval, 1999,
p. 14; p. 17). One way to go about it is to present students with tasks like task 2 (identify
inflection points on given graphs) and like task 6 identify inflection points of f (x) by
examining the graph of f ′ (x), the latter being more complex and demanding. Duval also
referred to the importance of learners’ flexible mathematical performances when roles of
source register and target register are inverted within a semiotic representation conversion
task. For such purposes, we would suggest, for instance, tasks that present a graph (e.g.,
graph 3 in task 2 in the questionnaire), asking to mark on it inflection points and to explain
the solutions (conversion from graphic to verbal representation). Then, to present the
students with a verbal (or symbolic) representation of a related function (e.g., a function
that has an identical graph), asking the students to investigate the function, to find inflection
points, to complete the investigation and draw the graph, and finally to reflect on the entire
activity. Another example for such engagement is task 1, presented in this section.
In the mathematics education community, there is wide agreement that common errors
should play a significant role in teaching–learning processes, for example, in the design of
pre-teaching, diagnostic tools for identifying students’ difficulties and in the design of
related instruction (e.g., Fischbein, 1987; Greeno, Collins &amp; Resnick, 1996). Several
researchers encourage teachers to challenge students’ mathematical reasoning by asking
them to justify the correctness of given solutions or to identify the bugs in incorrect ones
(Borasi, 1996). Here is a task (c.f. Mason &amp; Watson, 2001, p. 128), addressing the error “f ″
(x)=0 is sufficient for inflection points.”
A common method for finding inflection points of a curve which is at least twice
differentiable is to differentiate twice and set equal to zero to find the abscissa.
Sometimes this gives a correct answer for a correct reason, sometimes it gives a
correct answer for a wrong reason, and sometimes it gives an incorrect answer.
Construct examples which exemplify these three situations, and also a family of
examples which include all three in each member.
Must a function be twice differentiable to have an inflection point?
We illustrated some ways to implement our data in designing instruction. Clearly, there
are additional instructional ideas, and it would be wise to examine what instructional
approaches are efficient in promoting students’ knowledge and in increasing their awareness
of and abilities to control their pseudo-mathematical, intuitive ideas.
References
Artigue, M. (1992). The importance and limits of epistemological work in didactics. In W. Geeslin &amp; K.
Graham (Eds.), Proceedings of the 16th Conference of the International Group for the Psychology of
Mathematics Education, (Vol. 3, pp. 195–216). Durham, NH.
Balacheff, N. (1987). Processus de preuve et situations de validation [Proof processes and situations of
validation]. Educational Studies in Mathematics, 18, 147–176.
Bell, A. W. (1976). A study of pupil’s proof-explanations in mathematical situations. Educational Studies in
Mathematics, 7, 23–40.
Borasi, R. (1996). Reconceiving mathematics instruction: A focus on errors. New Jersey: Ablex.
P. Tsamir, R. Ovodenko
Carlson, M., Jacobs, S., Coe, E., Larsen, S., &amp; Hsu, E. (2002). Applying covariational reasoning while
modeling dynamic events: A framework and a study. Journal for Research in Mathematics Education, 33,
352–378.
Duval, R. (1999). Representation vision and visualization: Cognitive functions in mathematical thinking—Basic
issues for learning. In H. Fernando &amp; S. Manuel (Eds.), Proceedings of the 21st annual meeting of the North
American Chapter of the International Group for the Psychology of Mathematics Education (pp. 3–26).
Morelos: Columbus, OH: ERIC Clearinghouse for Science, Mathematics, and Environmental Education.
Duval, R. (2000). Basic issues for research in mathematics education. In T. Nakahara &amp; M. Koyama (Eds.),
Proceedings of the 24th annual meeting of the International Group for the Psychology of Mathematics
Education (Vol. 1, pp. 55–69). Hiroshima, Japan: Nishiki Print Co. Ltd.
Duval, R. (2002). The cognitive analysis of problems of comprehension in the learning of mathematics.
Mediterr J Res Math Educ, 1(2), 1–16.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics.
Educational Studies in Mathematics, 61, 103–131.
Fischbein, E. (1987). Intuition in science and mathematics: An educational approach. Dordrecht: Reidel.
Fischbein, E. (1993a). The interaction between the formal and the algorithmic and the intuitive components in
a mathematical activity. In R. Biehler, R. W. Scholz, R. Straser, &amp; B. Winkelmann (Eds.), Didactics of
mathematics as a scientific discipline (pp. 231–345). Dordrecht: Kluwer.
Fischbein, E. (1993b). The theory of figural concepts. Educational Studies in Mathematics, 24(2), 139–162.
Fischbein, E., &amp; Barash, A. (1993). Algorithmic models and their misuse in solving algebraic problems.
Proceedings of the 17th Conference of the International Group for the Psychology of Mathematics
Education, (vol. 1, pp. 162–172).
Gomez, P., &amp; Carulla, C. (2001). Students’ conceptions of cubic functions. Proceedings the 25th Conference
of the International Group for the Psychology of Mathematics Education (vol. 3, pp. 56–64).
Greeno, J. G., Collins, A. M., &amp; Resnick, L. B. (1996). Cognition and learning. In D. C. Berliner &amp; R. C.
Calfee (Eds.), Handbook of educational psychology (pp. 15–45). NY: Simon &amp; Schuster Macmillan.
Harel, G., &amp; Sowder, L. (2007). Toward comprehensive perspectives on the learning and teaching of proof. In
F. K. Lester Jr. (Ed.), Second handbook of research on mathematics teaching and learning (pp. 805–842).
Greenwich, CT: Information Age.
Hesselbart, A. (2007). Mathematics reasoning and semiosis. Denmark: University of Copenhagen. http://
www.ind.ku.dk/publikationer/studenterserien/studenterserie4/thesisery5-medbundtekstmedforside.pdf.
Mason, J. (2001). Teaching for flexibility in mathematics: Being aware of the structures of attention and
intention. Quaestiones Mathematicae, 24, 1–15.
Mason, J., &amp; Watson, A. (2001). Stimulating students to construct boundary examples. Quaestiones
Mathematicae, 24, 123–132.
Monk, G. (1992). Students’ understanding of a function given by a physical model. In G. Harel &amp; E.
Dubinsky (Eds.), The concept of function: Aspects of epistemology and pedagogy, MAA notes, 25 (pp.
175–194). Washington, DC: Mathematical Association of America.
Nardi, E. (2008). Amongst mathematicians: Teaching and learning mathematics at university level. NY:
Springer.
Nemirovsky, R., &amp; Rubin, A. (1992). Students’ tendency to assume resemblances between a function and its
derivative. Cambridge: Unpublished manuscript.
Ovodenko, R., &amp; Tsamir, P. (2005). Possible causes of failure when handling the notion of inflection point.
Proceedings of the 4th Colloquium on the Didactics of Mathematics (pp. 77–89).
Selden, J., &amp; Selden, A. (1995). Unpacking the logic of mathematical statements. Educational Studies in
Mathematics, 29, 123–151.
Tall, D. (1987). Constructing the concept image of a tangent. Proceedings of the 11th Conference of the
International Group for the Psychology of Mathematics Education (vol. 3, pp. 69–75).
Tall, D., &amp; Vinner, S. (1981). Concept image and concept definition in mathematics, with special reference to
limits and continuity. Educational Studies in Mathematics, 12, 151–169.
Tsamir, P. (2007). Should more than one theoretical approach be used for analyzing students’ errors? The case
of areas, volumes and integration. Learn Math, 27(2), 28–33.
Tsamir, P. (2008). Using theories as tools in teacher education. In D. Tirosh &amp; T. Wood (Eds.), International
handbook of mathematics teacher education: Tools and processes in mathematics teacher education (Vol.
3, pp. 211–233). Rotterdam, the Netherlands: Sense Publishers.
Tsamir. P., &amp; Ovodenko, R. (2004). Prospective teachers’ images and definitions: The case of inflection
points. Proceedings of the 28th Conference of the International Group for the Psychology of
Mathematical Education, (Vol. 4, pp. 337–344).
Vinner, S. (1982). Conflicts between definitions and intuitions—The case of the tangent. Proceedings of the
6th International Conference for the Psychology of Mathematical Education (pp. 24–29).
University students’ grasp of inflection points
Vinner, S. (1990). Inconsistencies: Their causes and function in learning mathematics. Focus on Learning
Problems in Mathematics, 12(3–4), 85–98.
Vinner, S. (1991). The role of definitions in the teaching and learning of mathematics. In Tall, D.O. (Ed.),
Advanced mathematical thinking, (pp. 65–81). Dordrecht, Kluwer.
Vinner, S. (1997). The pseudo-conceptual and pseudo-analytical thought processes in mathematics learning.
Educational Studies in Mathematics, 34, 97–129.
Vinner, S., &amp; Dreyfus, T. (1989). Images and definitions for the concept of function. Journal for Research in
Mathematics Education, 20(4), 356–366.
Weber, K. (2001). Students’ difficulty in constructing proof: The need for strategic knowledge. Educational
Studies in Mathematics, 48, 101–119.
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