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Index: modules/gerd/discussions/paper/discussions.tex
diff -u modules/gerd/discussions/paper/discussions.tex:1.34 modules/gerd/discussions/paper/discussions.tex:1.35
--- modules/gerd/discussions/paper/discussions.tex:1.34	Thu Jan  5 10:38:45 2006
+++ modules/gerd/discussions/paper/discussions.tex	Thu Jan  5 12:13:39 2006
@@ -435,7 +435,7 @@
 \end{figure*}
 
 \begin{table}
-\caption{Same as Table~\ref{table:disccat} for the first semester calculus-based class only. The table includes a small number of contributions by students who eventually dropped the course, which were included in the analysis by problem type, but not in the analysis by student characteristics.\label{table:disccatfirst}}
+\caption{Same as Table~\ref{table:disccat} for the first semester of the calculus-based class only. The table includes a small number of contributions by students who eventually dropped the course, which were included in the analysis by problem type, but not in the analysis by student characteristics.\label{table:disccatfirst}}
 \begin{ruledtabular}
 \begin{tabular}{lcccccccc|l}
 &\multicolumn{2}{c}{Emotional}
@@ -452,7 +452,7 @@
 \end{tabular}
 \end{ruledtabular}
 \end{table}
-Within the first semester calculus-based course, an analysis by student characteristics was performed. Table~\ref{table:disccatfirst} shows the
+Within the first semester of the calculus-based course, an analysis by student characteristics was performed. Table~\ref{table:disccatfirst} shows the
 equivalent of Table~\ref{table:disccat} for this subset of the data. Out of the 211 students who completed the course,
 138 students (65 percent) contributed at least one discussion posting over the course of the semester. Figure~\ref{fig:contribBinned} shows the distribution
 of number of discussion contributions over the course of the semester. Most students who participated made between one and ten contributions, but one student made
@@ -471,12 +471,17 @@
 \end{figure}
 
 While the {\it number} of postings is uncorrelated to course grade, their {\it classification} 
-(subsection~\ref{subsec:disccat}) is correlated. 
-In this analysis, the percentage of prominence of certain classes
-in students' cumulative contributions over the semester was analyzed. The individual percentage (relative) prominences were then averaged by grade. 
+(subsection~\ref{subsec:disccat}) turns out to be correlated: 
+In this analysis, the percentage of prominence of certain classes or superclasses
+in students' cumulative contributions over the semester was analyzed, i.e., the percentage of the respective student's discussion contributions across all problems that belonged into a certain class
+or superclass. 
 Note that the outcome is independent of the absolute number of postings a student made, e.g., the discussion behavior of the student who made 66 contributions is weighed 
-equally to that of a student having made only the average 5 contributions. Figure~\ref{fig:gradecorrel}
-shows the outcome of this study by discussion superclass. As an example, the figure is to be interpreted this way: within the indicated errors, 
+equally to that of a student having made only the average 5 contributions. 
+
+
+For each grade, the individual percentage (relative) prominences of these classes for students with that grade were averaged.
+Figure~\ref{fig:gradecorrel}
+shows the outcome of this study by discussion superclass. For example, the figure is to be interpreted this way: within the indicated errors, 
 55 percent of a 3.0 student's discussion contributions were solution-oriented. The lines represent second-order polynomial fits to the data.
 
 The relative prominence of solution-oriented discussion contributions varies most strongly with grade, from 75 percent for a 2.0 student to 45 percent for a 4.0 student.
@@ -490,9 +495,9 @@
 section~\ref{sec:question}, particular attention needs to be paid to problem properties that elicit either the desirable or undesirable discussion behavioral patterns.
 
 Due to the smaller sample size, a correlation analysis by the individual ``question" and ``answer" classes yielded no statistically significant results. 
-\section{Results of Analysis by Problem\label{sec:question}}
+\section{Results of Analysis by Problem Characteristics\label{sec:question}}
 \subsection{Influence of Problem Difficulty}
-Using the full data set of three courses, each discussion contribution associated with a problem was classified according to 
+Using the full data set of all three courses, each discussion contribution associated with a problem was classified according to 
 subsection~\ref{subsec:disccat}. As a measure of the prominence of a class in a given discussion, 
 the number of contributions belonging to it is divided by the total number of contributions. The discussion characteristics of the problems were binned by their 
 difficulty index and the average percentage plotted in figure~\ref{fig:diff}. Only superclasses are
@@ -533,7 +538,7 @@
 \end{figure}
 
 \subsection{\label{subsec:qtype}Influence of Problem Types}
-Using the full data set of three courses, each problem was classified according to subsection~\ref{subsec:problemcat}, and each associated discussion entry according to~\ref{subsec:disccat}. As a measure of the prominence of a class in a given discussion, 
+Using the full data set of all three courses, each problem was classified according to subsection~\ref{subsec:problemcat}, and each associated discussion entry according to~\ref{subsec:disccat}. As a measure of the prominence of a class in a given discussion, 
 the number of contributions belonging to it is divided by the total number of contributions. 
 Table~\ref{table:qtype} shows the percentage prominence of discussion contributions with a certain type or with certain features in the discussions associated with problems
 that are of a certain type or have certain features. 
@@ -631,10 +636,11 @@
 The presented method to gain insight into student problem solving behavior is comparable to the more traditional ``thinking out loud'' or group discussion observations. 
 However, in the former method, the subjects are keenly aware of the observer, which may influence their behavior: in most any course, appropriate problem solving
 techniques would have been discussed, and while in reality, students might find them ``inefficient" or ``slow"~\cite{lin}, they might try hard to exhibit them in the research setting. The latter method, observation of student discussions, is
-likely closer to the behavior students would exhibit when not observed, since would aim to solve the homework in the way they believe is most efficient. However, groups are smaller and in most studies interact around problems less complex than the average homework problem.
+likely closer to the behavior students would exhibit when not observed. However, groups are smaller, and in most studies interact around problems less complex than the average homework problem.
 
-An advantage for the researcher is the ready availability of the online discussions --- there is no need for transcription, since the discussions are already in textual form. In addition, since written student
+An advantage for the researcher of this method is the ready availability of the online discussions --- there is no need for transcription, since the discussions are already in textual form. In addition, since written student
 discussions contain less spurious verbiage and slang, and tend to exhibit better grammar and more complete sentences than the spoken word, evaluation is easier.
+The discussion contributions are likely to closely reflect students' actual approach, since students would aim to solve the homework in the way they believe is most efficient. 
 The large number of discussion contributions allows for statistically significant results.
 
 In the current study, a general classification scheme was deployed across physics topics and concepts. However, since the discussions are associated with certain problems, they can be used to study student
@@ -656,7 +662,11 @@
 \section{Outlook}
 In this current study, little is known about the students except their gender and final course grade, and the analysis of discussion behavior by student characteristics thus yielded less results than the study by problem characteristics.
 Research~\cite{aleven} suggests that learning processes are strongly influenced by epistemological beliefs, and it will be interesting to analyze the correlation between attitudes and beliefs (as measured for example by the MPEX~\cite{mpex}) regarding physics and online discussion behavior.
-Also, the final grade in the course incorporates a lot of factors, and interactions with for example the FCI~\cite{fci} gains might result in better correlations to the students' conceptual understanding of physics.
+Also, the final grade in the course incorporates a lot of factors including some measures of simply diligence, and interactions with for example the FCI~\cite{fci} gains might result in better correlations to the students' conceptual understanding of physics. 
+
+Reliable and consistent coding of discussion contributions is crucial for the analysis. Any future study should include more than one instructor in coding the discussion contributions to 
+increase reliability and generalizability of the results, as well as to avoid possible personal bias. As the online material in the courses changes, more and more numerical problems are
+replaced or enhanced by other problems types, which hopefully will yield more statistically significant results.
 \begin{acknowledgments}
 Supported 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.

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