[prev in list] [next in list] [prev in thread] [next in thread]
List: lon-capa-cvs
Subject: [LON-CAPA-cvs] cvs: modules /gerd/correlpaper correlations.tex
From: www <lon-capa-cvs () mail ! lon-capa ! org>
Date: 2006-09-29 19:13:28
Message-ID: cvswww1159557208 () cvsserver
[Download RAW message or body]
This is a MIME encoded message
www Fri Sep 29 15:13:28 2006 EDT
Modified files:
/modules/gerd/correlpaper correlations.tex
Log:
Really, never ending. But shorter for now.
["www-20060929151328.txt" (text/plain)]
Index: modules/gerd/correlpaper/correlations.tex
diff -u modules/gerd/correlpaper/correlations.tex:1.12 \
modules/gerd/correlpaper/correlations.tex:1.13
--- modules/gerd/correlpaper/correlations.tex:1.12 Thu Sep 28 11:18:57 2006
+++ modules/gerd/correlpaper/correlations.tex Fri Sep 29 15:13:27 2006
@@ -69,11 +69,16 @@
The MPEX ``Product Warning Label'' continues, that ``this survey is primarily \
intended to evaluate the impact of one or more semesters of instruction on an overall \
class''~\cite{mpexwarning}, and recommends using the outcomes, in combination with \
evaluations of student learning of content, as a means to improve overall course \
instruction. In this paper, we are asking the question if the evaluation of student \
online discussion behavior can be used as a means to get to assess the attitudes and \
beliefs of an individual student, and if in turn, these can be used to predict the \
success of an individual student in the learning of physics content. An obvious \
application would be the early detection of students at risk.
+In particular:
+\begin{itemize}
+\item We classify the online homework discussion contributions from one course
+\item We deploy the MPEX for comparison as a pre- and post-test
+\item We are using the pre- and post-FCI, as well as the final exam and course \
grades, as a measure of student learning +\end{itemize}
+
\section{\label{background}Background}
Online discussions are a rich source of feedback to the \
instructor~\cite{kortemeyer05feedback}, and their quality and character was found to \
be correlated with the type and difficulty of the associated \
problems~\cite{kortemeyer05ana}, i.e., data exists regarding the influence of {\it \
problem} characteristics on associated discussions. Unfortunately, less data exists \
on the correlation between {\it student} characteristics and discussion behavior, \
because usually only very few student characteristics are known, with the exception \
of the students' overall performance in the course. Thus, one of the few findings was \
the fact that certain discussion behavior, most prominently exhibited on \
``non-sanctioned'' discussion sites external to the course, is negatively correlated \
with performance in the course~\cite{kashy03,kortemeyer05ana}. Also, \
Hogan~\cite{hogan99} assessed eight graders' epistemological frameworks through \
interviews and then analyzed their discussion behavior in a ! science course with a \
particular focus on collaboration, finding a number of correlations.
-In this study, we aim to answer the question if and how student discussion \
characteristics are related to their beliefs, attitudes, and expectations, as \
measured by the MPEX. We investigate correlations with the MPEX, and compare \
correlations with measures of student learning.
-
\section{\label{setting}Setting}
The project was carried out in an introductory calculus-based physics course with \
initially 214 students. Most of the students in this course plan on pursuing a career \
in a medical field. The course had three traditional lectures per week. It did not \
use a textbook, instead, all course materials were available online. Topics were \
introductory mechanics, as well as sound and thermodynamics. There was twice-weekly \
online homework: one small set as reading problems due before the topic was dealt \
with in class (implementing JiTT~\cite{jitt}), and a larger set of traditional \
end-of-the-chapter style homework at the end of each topic. The online problems in \
the course were randomized using the LON-CAPA system, i.e., different students would \
receive different versions of the same problem (different graphs, numbers, images, \
options, formulas, etc)~\cite{loncapa,kashyd01}. The students had weekly recitation \
sessions, and a traditional lab was offered in parallel. The course grade wa! s \
determined from the students' performance on biweekly quizzes, the final exam, the \
recitation grades, and the homework performance.
@@ -82,17 +87,14 @@
In LON-CAPA, discussions boards are directly underneath each online homework \
problem, i.e., each problem has its own discussion board on the same web page. Since \
problems in LON-CAPA are randomized, such that each student has different options, \
numbers, graphs, equations, or even scenarios (e.g., accelerating to the left or to \
the right), the students cannot simply exchange the correct answer, and are \
encouraged to freely discuss the problems with each other. Figure~\ref{newfig1} shows \
a screenshot of such a problem and its associated discussion. \begin{figure*}
\includegraphics[width=18cm]{newfig1}
-\caption{\label{newfig1}Example of an online homework problem with associated \
student discussions. The problem is past its due date, so the correct answer is \
shown. If it were still open, the students would have one text answer box, into which \
they would enter the value with physical units.} +\caption{\label{newfig1}Example of \
an online homework problem with associated student discussions. The problem is past \
its due date, so the correct answer is shown. If it were still open, the students \
would have one text answer box, into which they would enter the value with physical \
units. The students in this example chose to post anonymously, however, the student \
name is still available to faculty.} \end{figure*}
-The author analyzed the online student discussions that were associated with the \
online homework given in his course, using the scheme first suggested in \
Ref.~\cite{kortemeyer05ana}. There were a total of 2405 such online discussion \
contributions over the course of the semester.
-
-Each contribution was classified by the author according to the classification \
scheme of Ref.~\cite{kortemeyer05ana}, however, with the additional refinement that \
each contribution could be member of more than one class, and that the contributions \
were weighted by their length. For example, a certain contribution might include both \
a procedural solution-oriented question and a surface-level mathematical answer, and \
would thus receive 50\% membership in both classes, weighted by its total length. The \
student names were not available during classification in order to avoid bias. +The \
author analyzed the online student discussions that were associated with the online \
homework given in his course, using the scheme first suggested in \
Ref.~\cite{kortemeyer05ana}. The student names were not available during \
classification in order to avoid bias. +There were a total of 2405 such online \
discussion contributions over the course of the semester.
-The analysis was carried out based on discussion \
superclasses~\cite{kortemeyer05ana}, for example, all conceptual classes were \
combined, independent of their features. A given contribution can thus belong to more \
than one superclass.
-
-The following list shows the superclasses taken into consideration, as well as \
illustrative examples that have partial membership in each superclass, taken from \
this course: +The following list shows the classifications taken into consideration, \
as well as illustrative examples that would receive the respective classification. \
\begin{itemize}
\item Discussion contributions were classified as {\it surface} if they
@@ -185,8 +187,15 @@
That's why we float on the moon...
\end{quote}
\end{itemize}
+
+One particular contribution could receive more than one classification. Each \
contribution was weighed by its length when calculating the overall discussion \
behavior of an individual student. For additional details, see \
Ref.~\cite{kortemeyer05ana}, where the above classes were referred to as \
``superclasses.'' +
Note that correctness of the contribution was not considered. For example, in the \
last physics-related example, the fact that the student confused the gravitational \
constant and the gravitational acceleration was not taken into account.
+Interrater reliability was assessed by asking a graduate student in physics \
education research at another university to read +Ref.~\cite{kortemeyer05ana} and \
classify a randomly selected subset of 104 contributions. Without further training, \
the overall reliability was 81 percent. As it turned out, though, this value did vary \
by class: the conceptual and mathematical classes had an interrater reliability of 91 \
and 96 percent, respectively, while the solution-oriented class had a reliability of \
only 58 percent. Looking at the prominence of contributions in certain classes, the \
author classified 60 percent and the graduate student only 38 percent as \
``solution-oriented.'' The discrepancy might be due to the fact that the author in \
his course attempts to strongly discourage this behavior and thus may be more prone \
to detect and label it than an individual who is not connected with the course. The \
discrepancy could likely be resolved with training. +
+
As already found in Ref.~\cite{kortemeyer05ana}, most students are quite prolific in \
their online discussions, but a few students only made a small number of \
contributions, leading to small statistics on their actual discussion behavior. For \
each of the discussion correlations, we thus also carried out a second calculation \
limited to students who contributed at least five entries over the course of the \
semester.
\subsection{\label{mpex}The MPEX}
@@ -218,134 +227,45 @@
\end{quote}
\end{itemize}
-
-
\subsection{\label{performance}Measures of Student Learning}
As a measure of student conceptual understanding and learning, we deployed the \
revised Force Concept Inventory (FCI)\cite{fci} at the beginning and the end of the \
course, again with voluntary participation. As an additional measure of student \
performance, the performance on the final exam and the course grade for each student \
were taken into consideration. For the grade we used the raw percentage score, not \
the number grades, since it provides finer grained information about the overall \
student performance in the course.
-\section{\label{hypo}Hypotheses}
-Between the measures described in section~\ref{measures}, a number of correlations \
are to be expected:
-\begin{enumerate}
-\item Student responses to clusters on the MPEX should correlate with corresponding \
discussion behavior patterns
-\item Student performance on the FCI should correlate positively with desirable and \
negatively with undesirable discussion behavior
-\item Student performance on the FCI should positively correlate with performance on \
the MPEX
-\end{enumerate}
-Somewhat less general, since dependent on the grading mechanism implemented by the \
instructor, corresponding correlations should exist with the final exam and the \
course grade.
-\section{\label{results}Results}
-In this section, we present the correlations among the different instruments and \
measures.
-\subsection{Correlation Table}
-Table~\ref{fullresults} shows the complete correlation results of the study. In the \
columns of the table, we listed:
-grade performance;
-final exam performance;
-final (post) FCI score;
-FCI gain;
-final (post) MPEX score;
-MPEX gain;
-prominence of solution-oriented discussion contributions;
-prominence of math-related discussion contributions;
-prominence of physics-related discussion contributions;
-prominence of surface-level discussion contributions;
-prominence of procedural discussion contributions;
-prominence of conceptual discussion contributions.
-
-In the rows of the table, we listed:
-grade performance;
-final exam performance;
-final (post) FCI score;
-FCI gain;
-final (post) MPEX score;
-MPEX gain;
-MPEX Independence Cluster score;
-MPEX Coherence Cluster score;
-MPEX Concept Cluster score;
-MPEX Reality Link Cluster score;
-MPEX Math Link Cluster score;
-MPEX Effort Cluster score.
-
-Correlations with $|R|<0.1$ are indicated by a dash, correlations with $|R|>0.5$ are \
printed boldface. The values in brackets are the result of calculations limited to \
students with at least five discussion contributions over the course \
of the semester.
-
-\begin{table*}
-\caption{\label{fullresults}Complete correlation results ($R$-values). In the \
columns we list the grade performance, final exam performance, FCI scores and gains, \
MPEX scores and gains, as well as the prominences of different online discussion \
characteristics. In the rows we list the grade performance, final exam performance, \
FCI scores and gains, MPEX scores and gains, as well as the performance on the \
different MPEX clusters. Calculated correlations whose absolute value was lower than \
0.1 are indicated by "---." Correlations with an absolute value higher than 0.5 have \
been printed in boldface. The values given in brackets have been calculated including \
only students who contributed more than five discussion entries over the course of \
the semester. As an example, the correlation between the prominence of \
solution-oriented discussion contributions of students who made more than five \
contributions over the course of the semester and the FCI gain is -0.44, indicating \
that stu! dents who make more solution-oriented discussion contributions have a \
lower gain on the FCI between the beginning and the end of the \
semester.}
-\begin{ruledtabular}
-\begin{tabular}{rllllllllllll}
- &Grade&Final&FCI &FCI &MPEX&MPEX&Solution &Math &Physics \
&Surface &Proce-&Concep-\\
- & &Exam&Final &Gain &Final &Gain & \
& & & &dural \
&tual\\
-Grade & & &{\bf 0.56}&0.45 &0.3 &0.27 &--- \
(--0.25)&--0.15 (---)&0.22 (0.33)&--- (--0.25)&--- \
(---)&0.2 (0.28)\\
-Final & & && & & & & & & & & \\
-Exam & & & 0.46 & 0.39 & 0.27 & 0.26&--- (--0.16)&--- &0.15 \
(0.22)&--- (--0.18)&--- (0.1)&0.12 (0.16)\\
-FCI & & && & & & & & & & & \\
- Final &{\bf 0.56} &0.46 & & &0.24 &--- &--0.37 \
({\bf--0.58})&0.13 (0.1)&0.34 ({\bf 0.51})&--- (--0.41)&--- (---)&0.25 \
(0.35)\\
-FCI & & && & & & & & & & & \\
-Gain & 0.45 &0.39 & & &0.17 &--- &--0.28 \
(--0.44)&0.14 (---)&0.22 (0.4)&--0.1 (--0.34)&--- (---)&0.31 \
(0.34)\\
-MPEX & & && & & & & & & & & \\
-Final & 0.3 & 0.27 & 0.24 &0.17 & & &--0.12 \
(---)&--- (---)&0.17 (0.16)&--- (---)&--- \
(0.12)&0.13 (0.11)\\
-MPEX & & && & & & & & & & & \\
-Gain & 0.27 &0.26 & --- & --- & & &--- \
(---)&--- (---)&--- (0.18)&0.14 (---)&--0.14 (---)&--- \
(---)\\
-Indepen- & & && & & & & & & & & \\
-dence&0.25 &0.25&0.23 &0.1 & & &--- \
(---)&--- (0.13)&--- (---)&--- (---)&--- (---)&--- (0.15)\\ \
-Cohe- & & && & & & & & & & & \\
-rence &0.36 &0.31&0.2 &0.23 & & &--- \
(---)&--- (0.11)&0.18 (0.18)&--- (--0.18)&--- (0.24)&0.1 \
(0.17)\\
-Con-& & && & & & & & & & & \\
-cepts &0.25 &0.24 &0.21 &0.13 & & &--- \
(---)&--- (---)&0.11 (0.14)&--- (---)&--- \
(---)&0.14 (0.15)\\
-Reality & & && & & & & & & & & \\
-Link&--- &0.1&0.15 &--- & & &--0.12 \
(---)&--- (--0.16)&--- (---)&--0.2 (---)&0.11 \
(---)&--- (---)\\
-Math & & && & & & & & & & & \\
-Link &0.15 &---&0.13 &--- & & &--- \
(---)&--- (--0.14)&--- (---) &0.1 (---)&--- (---)&--- \
(---)\\
-Effort &0.15&0.15 &--- &0.11 & & &--0.14 \
(--0.19)&--- (---)&0.19 (0.2)&--0.14 (--0.12)&--- \
(---)&0.22 (0.15)
-\end{tabular}
-
-\end{ruledtabular}
-\end{table*}
-
-Of particular interest is the lower right corner of Table~\ref{fullresults}, as it \
lists the correlations between student attitudes and expectations (as measured by the \
MPEX clusters) with the prominence of discussion behavior classes. One would have \
expected strong correlations between for example the score on the Concepts Cluster \
and the prominence of conceptual discussion contributions ($R=0.14 [-0.08 - 0.34] \
(0.15 [-0.13 - 0.41]); n=84 (51)$), or the comfort level with the usage of \
mathematics as a language and the corresponding lack of purely mathematical \
contributions ($|R|<0.1$, and $R=-0.14 [-0.4 - 0.14]$ ($n=51$) when including only \
students with more than five contributions overall). However, the 95\% confidence \
intervals (given in square brackets) include zero. The Coherence and Effort Clusters \
are most strongly correlated with discussions, the Math Link Cluster -- surprisingly \
-- the least.
-
-The upper right and the lower left corner list the correlations of student \
discussion behavior and the MPEX, respectively, with measures of student learning. \
Correlations are again low, but of comparable magnitude, where the MPEX appears to be \
slightly more correlated with grade and final exam performance, while the discussion \
is more correlated with the FCI. In fact, some of the strongest correlations in the \
study occur between the prominence of solution-oriented and physics-related \
discussions and the FCI. We will analyze correlations with grades in more detail in \
subsection~\ref{gradecorrel}, and with the FCI in \
subsection~\ref{fcicorrel}.
-
-The Coherence Cluster of the MPEX appears to be more strongly correlated to other \
performance indicators than the other clusters. Out of that cluster, agreement with \
the statement "In doing a physics problem, if my calculation gives a result that \
differs significantly from what I expect, I'd have to trust the calculation" (53\% \
unfavorable responses) has $R=-0.3 [-0.47 - -0.11]$ ($n=97$) with the grade in the \
course, $R=-0.3 [-0.47 - -0.11]$ ($n=97$) with the final FCI Score, and $R=0.3 [0.09 \
- 0.48]$ ($n=84$) with solution-oriented discussion postings. Out of the Concepts \
Cluster, agreement with the single statement "The most crucial thing in solving a \
physics problem is finding the right equation to use" (45\% unfavorable responses) \
correlates with $R=-0.3 [-0.47 - -0.11]$ ($n=96$) with the final FCI score and also \
with $R=-0.3 [-0.48 - -0.09]$ ($n=85$) with the FCI Gain, i.e., stronger than the \
cluster it belongs to.
-
-Going beyond the analysis of the large discussion superclasses, when considering the \
intersection of student discussion characteristics, only a few relatively strong \
correlations can be found. For example, the prominence of discussion contributions \
that were both conceptual and physics-related correlates with $R=0.2 [0.05 - 0.33]$ \
($n=173$) with the grade in the course, and with $R=0.29 [0.09 - 0.46]$ ($n=95$) and \
$R=0.3 [0.09 - 0.48]$ ($n=84$) with the final FCI Score and Gain, respectively. The \
prominence of contributions that are both solution-oriented and surface-level \
correlates with $R=-0.29 [-0.46 - -0.09]$ ($n=95$) and $R=-0.13 [-0.34 - 0.08]$ \
($n=84$) with the FCI Score and Gain, respectively.
-
-
+\section{\label{results}Results}
+\subsection{\label{MPEXDiscussion}Correlations between Discussion Behavior and MPEX}
+To directly compare the attitudes and beliefs measures, we calculated correlations \
between the prominence of discussion behavior classes and the MPEX clusters, and \
generally found them to be very low. As an example, the correlation between the score \
on the Concepts Cluster and the prominence of conceptual discussion contributions \
turned out to be $R=0.14 [-0.08 \to 0.34]; n=84$ when considering all students, and \
$R=0.15 [-0.13 \to 0.41]; n=51$ when only considering those who made at least five \
discussion contributions -- the 95\% confidence intervals (given in square brackets) \
include zero. Thus, we conclude that discussion behavior and the individual MPEX \
cluster scores are -- if at all -- only weakly correlated.
+\subsection{\label{learningcorrel}Correlations between Discussions, MPEX, and \
Learning} +Correlations between the MPEX and measures of student learning are \
generally weak. Considering final exam and course grade, $R=0.36 [0.17 \to 0.52]$ \
($n=97$) between the score on the Coherence cluster and the course grade percentage \
is the highest correlation found. Dancy~\cite{dancy02} found similarly low \
correlations with the performance on homework, tests, and final exams: direct \
comparison with the performance on the final exams found $R=0.37$ for the correlation \
with the total MPEX score ($R=0.27$ here), $R=0.39$ with the Independence Cluster \
($R=0.25$ here), $R=0.24$ with the Coherence Cluster ($R=0.36$ here), $R=0.29$ with \
the Concept Cluster ($R=0.25$ here), $R=-.02$ with the Reality Link cluster ($R=0.1$ \
here), $R=0.3$ with the Math Link cluster (no significant correlation found here), \
and no significant correlation with the Effort Cluster ($R=0.1$ here). As a caveat \
already pointed out in section~\ref{setting}, however, the course grade is based on a \
number of fa! ctors, some of which are simply a matter of diligence or effort.
-\subsection{\label{gradecorrel}Correlations with the Overall Course Grade and Final \
Exam}
-Figure~\ref{fcimpexgrade} shows the correlation between the final FCI and MPEX \
scores with the final course grade percentage. With an $R$ of $0.56 [0.41 - 0.68]$ \
($n=110$) and $0.30 [0.11 - 0.47]$ ($n=97$), respectively, these -- particularly for \
the MPEX -- turned out lower than expected. As pointed out in section~\ref{setting}, \
however, the course grade is based on a number of factors, some of which are simply a \
matter of diligence or effort.
-
-\begin{figure*}
-\includegraphics[width=9cm]{fcipostgrade}\includegraphics[width=9cm]{mpexpostgrade}
-\caption{\label{fcimpexgrade}Correlation between the FCI score (left; $R=0.56 [0.41 \
- 0.68] $; $n=110$) and the MPEX score (right; $R=0.3 [0.11 - 0.47] $; $n=97$) with \
the course grade percentage. 58\% was the minimum percentage to pass the course. More \
students participated in the FCI than in the MPEX.}
-\end{figure*}
-
-Correlations with the MPEX were generally weak, with $R=0.36 [0.17 - 0.52]$ ($n=97$) \
between the score on the Coherence cluster and the course grade percentage being the \
highest value. Dancy~\cite{dancy02} found similarly low correlations with the \
performance on homework, tests, and final exams: direct comparison with the \
performance on the final exams found $R=0.37$ for the correlation with the total MPEX \
score ($R=0.27$ here), $R=0.39$ with the Independence Cluster ($R=0.25$ here), \
$R=0.24$ with the Coherence Cluster ($R=0.36$ here), $R=0.29$ with the Concept \
Cluster ($R=0.25$ here), $R=-.02$ with the Reality Link cluster ($R=0.1$ here), \
$R=0.3$ with the Math Link cluster (no significant correlation found here), and no \
significant correlation with the Effort Cluster ($R=0.1$ here).
-Figure~\ref{physicsgrade} shows the correlation between the prominence of \
physics-related discussions and the course grade percentage (for better statistics, \
only students who contributed at least five discussion entries over the course of the \
semester were considered). The correlation is stronger than with the MPEX Score, yet \
smaller than with the FCI. +Figure~\ref{mpexfci} shows how the final MPEX and FCI \
scores correlated with each other, i.e, $R=0.24 [0.04 \to 0.42]$ ($n=97$). +Coletta \
and Philips~\cite{coletta05} found a strong correlation between the FCI Gain and the \
MPEX Score ($R=0.52 [0.24 \to 0.72]; n=37$), while the same correlation turned out \
much lower in this study ($R=0.17 [-0.05 \to 0.37]; n=84$ here). The correlations \
reported here are in the same range that +Perkins et al.~\cite{perkins04} found when \
investigating the influence of beliefs on conceptual learning, using the \
CLASS~\cite{adams04} and the Force and Motion Conceptual Evaluation \
(FMCE)~\cite{thornton98} instruments. \begin{figure}
-\includegraphics[width=9cm]{physicsgrade}
-\caption{\label{physicsgrade}Correlation of percentage physics-related discussions \
with grade percentage ($R=0.33 [0.15 - 0.49]$; $n=111$).} \
+\includegraphics[width=9cm]{fcipostmpexpost} +\caption{\label{mpexfci}Correlation of \
the final FCI score with the MPEX score ($R=0.24 [0.04 \to 0.42]$; $n=97$).} \
\end{figure}
-\subsection{\label{fcicorrel}Correlations with the FCI}
-Figure~\ref{mpexfci} shows how the final FCI and MPEX scores correlated with each \
other, i.e, $R=0.24 [0.04 - 0.42]$ ($n=97$).
-Coletta and Philips~\cite{coletta05} found a strong correlation between the FCI Gain \
and the MPEX Score ($R=0.52 [0.24 - 0.72]; n=37$), while the same correlation turned \
out much lower in this study ($R=0.17 [-0.05 - 0.37]; n=84$ here). The correlations \
reported here are in the same range that
-Perkins et al.~\cite{perkins04} found when investigating the influence of beliefs on \
conceptual learning, using the CLASS~\cite{adams04} and the Force and Motion \
Conceptual Evaluation (FMCE)~\cite{thornton98} instruments.
-
-Correlations with discussion characteristics turned out somewhat stronger. \
Figure~\ref{fciphysics} shows how the percentage of a particular student's discussion \
contribution that was classified as "physics-related" correlates with their final FCI \
score ($R=0.34 [0.15 - 0.51]$; $n= 95$). As already in Fig.~\ref{physicsgrade}, an \
additional analysis was carried out that was limited to students for which better \
statistics were available, which let to a stronger correlation ($R=0.51[0.29 - \
0.68]$; $n=57$). +Figure~\ref{physicsgrade} shows the correlation between the \
prominence of physics-related discussions and the course grade percentage (for better \
statistics, only students who contributed at least five discussion entries over the \
course of the semester were considered). The correlation is stronger than with the \
MPEX Score, yet smaller than with the FCI.
\begin{figure}
-\includegraphics[width=9cm]{fcipostmpexpost}
-\caption{\label{mpexfci}Correlation of the final FCI score with the MPEX score \
($R=0.24 [0.04 - 0.42]$; $n=97$).} +\includegraphics[width=9cm]{physicsgrade}
+\caption{\label{physicsgrade}Correlation of percentage physics-related discussions \
with grade percentage ($R=0.33 [0.15 \to 0.49]$; $n=111$).} \end{figure}
+Figure~\ref{fciphysics} shows how the percentage of a particular student's \
discussion contribution that was classified as "physics-related" correlates with \
their final FCI score ($R=0.34 [0.15 \to 0.51]$; $n= 95$). As already in \
Fig.~\ref{physicsgrade}, an additional analysis was carried out that was limited to \
students for which better statistics were available, which let to a stronger \
correlation ($R=0.51[0.29 \to 0.68]$; $n=57$). While physics-related discussions \
positively correlate with FCI scores and grades (Fig.~\ref{physicsgrade}), \
solution-oriented discussions negatively correlate (Fig.~\ref{solutionfci}; $R=-0.58 \
[-0.73 \to -0.38]$; $n=57$). +
\begin{figure*}
\includegraphics[width=9cm]{fcipostphysics}\includegraphics[width=9cm]{fcipostphysicsT}
-\caption{\label{fciphysics}Correlation between the FCI score and the percentage of \
that student's discussion that was classified as "physics" ($R=0.34 [0.15 - 0.51]$; \
$n= 95$). The figure on the right only includes students who contributed more than \
five discussion entries over the course of the semester ($R=0.51 [0.29 - 0.68]$; \
$n=57$).} +\caption{\label{fciphysics}Correlation between the FCI score and the \
percentage of that student's discussion that was classified as "physics" ($R=0.34 \
[0.15 \to 0.51]$; $n= 95$). The figure on the right only includes students who \
contributed more than five discussion entries over the course of the semester \
($R=0.51 [0.29 \to 0.68]$; $n=57$).} \end{figure*}
-While physics-related discussions positively correlate with FCI scores and grades \
(Fig.~\ref{physicsgrade}), solution-oriented discussions negatively correlate \
(Fig.~\ref{solutionfci}; $R=-0.58 [-0.73 - -0.38]$; $n=57$). \begin{figure}
\includegraphics[width=9cm]{fcipostsolutionT}
-\caption{\label{solutionfci}Correlation of percentage solution-oriented discussions \
with final FCI score ($R=-0.58 [-0.73 - -0.38]$; $n=57$).} \
+\caption{\label{solutionfci}Correlation of percentage solution-oriented discussions \
with final FCI score ($R=-0.58 [-0.73 \to -0.38]$; $n=57$).} \end{figure}
+
+
\section{Discussion of the Correlation Results}
Correlations between Grade, Final Exam, FCI, MPEX, and student discussion behavior \
have turned out lower than expected. The strongest correlations exist with the final \
score on the FCI, namely $R=0.56$ with the grade percentage in the course, $R=0.51$ \
with the prominence of physics-related discussions, and $R=-0.58$ with the prominence \
of solution-oriented discussions.
@@ -398,7 +318,7 @@
The expected correlation between MPEX clusters and the prominence of different \
classes of student discussion behavior is largely missing. The reason for this lack \
of correlation could not completely be determined in the framework of this study: it \
might be that the mechanisms -- even in related areas -- measure different things, or \
that at least one of them in fact measures very little, or that, as indicated by an \
additional survey, the students did not bother responding to the MPEX with sufficient \
diligence. \begin{acknowledgments}
Supported in part by the National Science Foundation under NSF-ITR 0085921 and \
NSF-CCLI-ASA 0243126. Any opinions, findings, and conclusions or recommendations \
expressed in this
-publication are those of the author and do not necessarily reflect the views of the \
National Science Foundation. The author would like to thank the students in his \
course for their participation in this study. +publication are those of the author \
and do not necessarily reflect the views of the National Science Foundation. The \
author would like to thank the students in his course for their participation in this \
study, as well as Deborah Kashy for assistance with the statistical analysis of the \
data. \end{acknowledgments}
\bibliography{correlations}% Produces the bibliography via BibTeX.
_______________________________________________
LON-CAPA-cvs mailing list
LON-CAPA-cvs@mail.lon-capa.org
http://mail.lon-capa.org/mailman/listinfo/lon-capa-cvs
[prev in list] [next in list] [prev in thread] [next in thread]
Configure |
About |
News |
Add a list |
Sponsored by KoreLogic