Results (Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)
In order to answer their first research question(influence of previous grades, course duration, type of assignment ,forum variables and data collection procedure), the authors have developed several models with different features and found that previous grades were the strongest predictors and predictive power improves over time. Additionally, forum variables have relatively low predictive power. Regarding the second research question (effect of clickstream data and variables related to exercises on prediction) it was found that the clickstream data doesn't give the models better predictive power alongside with courseware data and only analyzing courseware data can be enough. In case if exercise data isn't available, clickstraem data can be useful. The next question was related with how the type of question can affect the final exam performance, like whether the questions are close ended or open-ended. It was found that predictive power of open ended questions were lower compared to multiple choice questions. Final exam and final grade are highly correlated (0.95) and it was found that average knowledge in the long term is more difficult to predict than actual knowledge at the specific moment.
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Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics
Introduction (Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)
Related Work (Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)
Methodology Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)
Results (Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)
Discussion (Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics)