Dropout Model Evaluation in MOOCs

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions

MOOC Development in Basic Natural Sciences as a Distance Learning Solution

The Covid-19 pandemic that occurred massively in Indonesia had an impact on distance / online learning that must be done to reduce physical interaction between lecturers and students. Distance learning that has been done is still one-way communication. Development of the Massive Open Online Course (MOOC) as a learning platform that can support distance learning. The purpose of this research is to develop MOOCs as support for distance learning, to determine the quality of the MOOC, and the level of MOOC satisfaction. The research method used is the ADDIE development model with phase analysis, design, development, implementation, dan evaluation. The results of the research at each stage are: analysis stage needs analysis and problem analysis on the need for distance learning solutions with active two-way communication. The design stage made a MOOC design with RPS, materials, videos, enrichment questions, and quizzes at every meeting. The development stage develops each menu stage in the MOOC from the expected competencies to the quiz. The implementation stage, validation of experts, and validation of small groups of students. The evaluation stage evaluates MOOCs both in language, layout, content, and user satisfaction. The conclusion of the study is that MOOC was developed with the stages of analysis, design, development, implementation, and evaluation (ADDIE). The MOOC quality from the validation results obtained an average of 80,8 in the good category. The results of the user satisfaction level of MOOC Basic Natural Sciences were 78% with a good categor