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Effectiveness of Machine Learning Al...

Ashby, Michael Wade,

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Effectiveness of Machine Learning Algorithms on Predicting Course Level Outcomes From Learning Management System Data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Effectiveness of Machine Learning Algorithms on Predicting Course Level Outcomes From Learning Management System Data // Michael Wade Ashby.
作者:	Ashby, Michael Wade,
面頁冊數:	1 electronic resource (160 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
標題:	Higher education administration. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31241309
ISBN:	9798383215685

Effectiveness of Machine Learning Algorithms on Predicting Course Level Outcomes From Learning Management System Data /
Ashby, Michael Wade,

Effectiveness of Machine Learning Algorithms on Predicting Course Level Outcomes From Learning Management System Data /Michael Wade Ashby. - 1 electronic resource (160 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Whether machine learning algorithms effectively predict college students' course outcomes using learning management system data is unknown. Identifying students who will have a poor outcome can help institutions plan future budgets and allocate resources to create interventions for underachieving students. Therefore, knowing the effectiveness of applying the algorithms to build models will be helpful in higher education institutions. This study utilizes the probably approximately correct learning theory, which posits machines can learn any concept as long as there is a data set of examples that labels the outcome of the concept, enough examples, and the computation can be completed in a polynomial number of steps. This quantitative comparative study compared four different machine learning algorithms' (naive Bayes, decision tree, neural network, and support vector machine) ability to predict the outcome of students in college courses by training models from learning management system data across two universities. It then measured the predictions of each model at a course level to determine their effectiveness. The results showed that the probably approximately correct learning theory works even in predicting course outcomes, as the decision tree successfully predicted students with poor outcomes with an F1 value above 0.5 and significantly better than the other three algorithms. Future studies can expand on the number of institutions involved in the contributing data and different learning management system data points as they may provide better predictions. Having learned the confidence of the decision tree's ability to predict students that will have poor outcomes, higher education institutions can better plan and allocate resources.

English

ISBN: 9798383215685Subjects--Topical Terms:

2122863
Higher education administration.
Subjects--Index Terms:

Decision tree

Effectiveness of Machine Learning Algorithms on Predicting Course Level Outcomes From Learning Management System Data /
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