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Evaluation of Recommended Learning Paths Using Process Mining and Log Skeletons: Conceptualization and Insight into an Online Mathematics Course

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Title: Evaluation of Recommended Learning Paths Using Process Mining and Log Skeletons: Conceptualization and Insight into an Online Mathematics Course
Language: English
Authors: Juan Antonio Martinez-Carrascal (ORCID 0000-0002-7696-6050), Jorge Munoz-Gama (ORCID 0000-0002-6908-3911), Teresa Sancho-Vinuesa (ORCID 0000-0002-0642-2912)
Source: IEEE Transactions on Learning Technologies. 2024 17:555-568.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Peer Reviewed: Y
Page Count: 14
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Online Courses, Mathematics Instruction, Undergraduate Students, Mathematics Achievement, Outcomes of Education, Learning Activities, Learning Trajectories, Grades (Scholastic), Correlation, Learning Analytics
DOI: 10.1109/TLT.2023.3298035
ISSN: 1939-1382
Abstract: Academic institutions dedicate a substantial effort to ensure the academic success of their students. At the course level, teachers recommend learning paths (RLPs) for students to guarantee the achievement of their learning outcomes. In terms of performance, these kinds of approaches are deemed more effective than others based uniquely on performing a collection of independent activities. However, there is neither systematic means to validate if following the learning path (LP) is effective, nor to assess whether and to what extent students adhere to these recommendations. This article introduces a novel technique for modeling recommended LPs, including not only an evaluation of path utility, but also a quantitative measure of student adherence thereto using process mining, and more precisely, log skeletons. Following an event abstraction process regarding real student-recorded activity, a scoping process is employed to retain the trajectories that adhere to the prescribed LP. The method based on process mining is translated into practice by considering an online university mathematics course. Results confirm the applicability of the method and, in this case, reveal that adhering to the suggested path correlates positively with final grades. Few students strictly follow the prescribed LP, although the vast majority support it. The method can be easily applied to overcome several challenges associated with enhancing academic performance from the learning analytics perspective.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1405362
Database: ERIC
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  Data: Evaluation of Recommended Learning Paths Using Process Mining and Log Skeletons: Conceptualization and Insight into an Online Mathematics Course
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  Data: <searchLink fieldCode="AR" term="%22Juan+Antonio+Martinez-Carrascal%22">Juan Antonio Martinez-Carrascal</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7696-6050">0000-0002-7696-6050</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jorge+Munoz-Gama%22">Jorge Munoz-Gama</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6908-3911">0000-0002-6908-3911</externalLink>)<br /><searchLink fieldCode="AR" term="%22Teresa+Sancho-Vinuesa%22">Teresa Sancho-Vinuesa</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0642-2912">0000-0002-0642-2912</externalLink>)
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  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
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  Data: Academic institutions dedicate a substantial effort to ensure the academic success of their students. At the course level, teachers recommend learning paths (RLPs) for students to guarantee the achievement of their learning outcomes. In terms of performance, these kinds of approaches are deemed more effective than others based uniquely on performing a collection of independent activities. However, there is neither systematic means to validate if following the learning path (LP) is effective, nor to assess whether and to what extent students adhere to these recommendations. This article introduces a novel technique for modeling recommended LPs, including not only an evaluation of path utility, but also a quantitative measure of student adherence thereto using process mining, and more precisely, log skeletons. Following an event abstraction process regarding real student-recorded activity, a scoping process is employed to retain the trajectories that adhere to the prescribed LP. The method based on process mining is translated into practice by considering an online university mathematics course. Results confirm the applicability of the method and, in this case, reveal that adhering to the suggested path correlates positively with final grades. Few students strictly follow the prescribed LP, although the vast majority support it. The method can be easily applied to overcome several challenges associated with enhancing academic performance from the learning analytics perspective.
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