کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
350793 618457 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Developing early warning systems to predict students’ online learning performance
ترجمه فارسی عنوان
سیستم های هشدار دهنده زود هنگام برای پیش بینی دانش آموزان؟ عملکرد یادگیری آنلاین
کلمات کلیدی
سیستم مدیریت یادگیری، آموزش الکترونیکی، سیستم اخطار سریع، داده کاوی، پیش بینی عملکرد یادگیری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• We develop early warning systems to predict at-risk students while a course is in progress.
• Learning portfolios from a fully online course are evaluated by data mining techniques.
• The results show that CART supplemented by AdaBoost has the best classification performance.
• Time-dependent variables are essential to identify student online learning performance.

An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students’ academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an “early warning” system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Human Behavior - Volume 36, July 2014, Pages 469–478
نویسندگان
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