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Titlebook: Educational Data Mining; Applications and Tre Alejandro Pe?a-Ayala Book 2014 Springer International Publishing Switzerland 2014 Computation

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41#
發(fā)表于 2025-3-28 18:25:48 | 只看該作者
Wolfgang Hossenfelder,Frank Schreyerdated using three approaches, two quantitative and one qualitative. While useful to individual instructors and administrators, it is only through data mining that student perceptions of teaching can be analyzed en masse to inform and influence the educational process.
42#
發(fā)表于 2025-3-28 19:22:57 | 只看該作者
43#
發(fā)表于 2025-3-29 02:05:02 | 只看該作者
A Survey on Pre-Processing Educational Datae pre-processing of educational data, Moodle data being mainly used in the examples. Next, it describes some general and specific pre-processing tools and finally, some conclusions and future research lines are outlined.
44#
發(fā)表于 2025-3-29 06:34:08 | 只看該作者
45#
發(fā)表于 2025-3-29 10:52:46 | 只看該作者
Using Data Mining Techniques to Detect the Personality of Players in an Educational Gamepply this framework, input excerpts must be classified into one of six possible personality classes. We applied this personality classification method using machine learning algorithms, such as: Naive Bayes, Support Vector Machine (SVM) and Decision Tree.
46#
發(fā)表于 2025-3-29 13:06:08 | 只看該作者
47#
發(fā)表于 2025-3-29 15:51:19 | 只看該作者
48#
發(fā)表于 2025-3-29 21:30:42 | 只看該作者
49#
發(fā)表于 2025-3-30 01:33:41 | 只看該作者
Adaptive Testing in Programming Courses Based on Educational Data Mining Techniquesd to guide the test creation process. Besides a genetic algorithm that selects a test most suitable to the aforementioned criteria, we present a concept map of programming competencies and a method of estimating the test item difficulty.
50#
發(fā)表于 2025-3-30 06:06:02 | 只看該作者
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