In the section, we will discuss about the experiment result from the learning forum that have been mentioned above, and describe the result of the statistical charts.
Result 1: The Result of “How” Document Class
Figure 5.2 The result with popularity in “How”
From the result we will see that in the “Statement” parts, about the “Loop”
learning, there are more problems for learners in the “for” loop then in the “while”
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loop in the C++ domain. It is reasonable that “for” loop are more hard than “while”
loop because the syntax in the “for” loop are more complex. In the “Data Structure”
parts, about the part “Abstract Data Type” learning, there are more problems in the
“Queue” and “List”, but there is no “Tree” or “Stack” in the “How” document classes.
There may be two reasons for the situation. One of the reasons is that “Stack” and
“Tree” are easier than the other two Data Structures. The other is that “Stack” and
“Tree” have not been teach at the time or “Stack” and “Tree” are not in the “How”
document class. Because the learning issue of the two kinds of the Data Structure and are not focused on how to use it but how to debug it.
Figure 5.3 The topic in “How”
The Figure 5.3 of the result are the authors published documents number in the topic “Loop, for”. As the topic shown, we can find that the topic documents are talking about the “for loop”, and with the bar chart, we can find that the learner
“tamdragon” post in the topic. Or the learner has more problems in the topic. It can conclude that the learner is more interested in the topic than other learners.
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Result 2: The result of “What” document class
Figure 5.4 the topics in “What”
In Figure 5.4, we can find out that the compositions in “What” are similar with
“How”. Actually, in the programming domain, after understanding a concept, learner is always interesting about how to use it.
Result 3: The result of “Why” document class
Figure 5.5 the topics in “Why”
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Result 4: The result of “Debug” Document Class
Figure 5.6 the topics in “Debug”
We can find that the topics in “Debug” are much different with the topics above.
The topic “New” are focused on. “Topic” new is talking about the dynamic allocation.
It is difficult for senior programming learners. Following figure is the detail in the
“New” topic.
Figure 5.7 The details in the “New” topic of “how”
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In the URLs list, we can find out that most of the documents in “New” are talking about the construction and destruction of dynamic memory allocation. Teachers may enhance the course on dynamic memory allocation.
Result 5: The result of the “New Type” documents
Figure 5.8 The topics in “New Type”
In the experiment, we have realized the different features in document classes by the Result 1~5. And the learning strategy that for different learner need can be proposed to teachers. We can also find the problems or the issues of learner that are popular. The trend of topics is showed to teachers.
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Chapter 6. Conclusion
According to the discussion above, an Ontology-based Topic Analyzer has been proposed for assisting teachers to realize the topics in the learning forum and give the topic trend to teachers.
The OTA includes the DKO to give the forum document the description of concepts and the adaptive clustering for topic extraction by different document classes, and the TV to view the topic trend with various dimensions including “Time”,
“Author”, “Location”, and “Popularity”.
With these parts, in this thesis, an Ontology-based Topic Analyzer including the Decision Table based Classifier, Ontology-based Adaptive Clusterer, and the Topic Viewer is implemented for giving the users a visualization report and charts for assisting the teachers to realize the global view of the topic analysis result.
In the experiment, we have proved that the approach of OTA for the learning forum is useful for teachers to realize the topic and the students‟ behavior.
In the near future, OTA will be ported to the different open sources forum systems, such as phpBB [1]in order for popularity.And Decision Table editor will be provided for Decision Table refining. Moreover, we will extend the ontology formats to the famous formats like OWL and RDF. Since the ontology construction and Decision Table editing will be easier, the user feedback model will also be provided for refining the Table of Classifier and the Domain Keyword Ontology for more precision topic analysis. The mechanism of analysis reporting will be improved for summarization by the results of statistic.
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