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- Publisher Website: 10.1145/3303772.3303781
- Scopus: eid_2-s2.0-85062781154
- WOS: WOS:000473277300039
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Conference Paper: Using Detailed Access Trajectories for Learning Behavior Analysis
Title | Using Detailed Access Trajectories for Learning Behavior Analysis |
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Authors | |
Keywords | Detailed access trajectory Learning pattern Marginalized learner Massive open online course Representation learning |
Issue Date | 2019 |
Publisher | Association for Computing Machinery (ACM). The Proceedings' web site is located at https://dl.acm.org/citation.cfm?id=3303772 |
Citation | Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK19), Tempe, AZ, USA, 4-8 March 2019, p. 290-299 How to Cite? |
Abstract | Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine granularity of the clickstream and coarse organizations that count activities, aggregate students or use long duration time units. A detailed access trajectory (DAT) consists of binary values and is two dimensional with one axis that is a time series, and the other that is a chronologically ordered list of a MOOC component type's instances, videos in instructional order, for example. Most popular MOOC platforms generate data that can be organized as detailed access trajectories (DATs). We explore the value of DATs by conducting four empirical mini-studies. Our studies suggest DATs contain rich information about students' learning behaviors and facilitate MOOC learning analyses. |
Persistent Identifier | http://hdl.handle.net/10722/275964 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Y | - |
dc.contributor.author | Law, NWY | - |
dc.contributor.author | Hemberg, E | - |
dc.contributor.author | O'Reilly, U | - |
dc.date.accessioned | 2019-09-10T02:53:13Z | - |
dc.date.available | 2019-09-10T02:53:13Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK19), Tempe, AZ, USA, 4-8 March 2019, p. 290-299 | - |
dc.identifier.isbn | 978-1-4503-6256-6 | - |
dc.identifier.uri | http://hdl.handle.net/10722/275964 | - |
dc.description.abstract | Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine granularity of the clickstream and coarse organizations that count activities, aggregate students or use long duration time units. A detailed access trajectory (DAT) consists of binary values and is two dimensional with one axis that is a time series, and the other that is a chronologically ordered list of a MOOC component type's instances, videos in instructional order, for example. Most popular MOOC platforms generate data that can be organized as detailed access trajectories (DATs). We explore the value of DATs by conducting four empirical mini-studies. Our studies suggest DATs contain rich information about students' learning behaviors and facilitate MOOC learning analyses. | - |
dc.language | eng | - |
dc.publisher | Association for Computing Machinery (ACM). The Proceedings' web site is located at https://dl.acm.org/citation.cfm?id=3303772 | - |
dc.relation.ispartof | 9th International Conference on Learning Analytics & Knowledge (LAK'19) | - |
dc.subject | Detailed access trajectory | - |
dc.subject | Learning pattern | - |
dc.subject | Marginalized learner | - |
dc.subject | Massive open online course | - |
dc.subject | Representation learning | - |
dc.title | Using Detailed Access Trajectories for Learning Behavior Analysis | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Law, NWY: nlaw@hku.hk | - |
dc.identifier.authority | Law, NWY=rp00919 | - |
dc.identifier.doi | 10.1145/3303772.3303781 | - |
dc.identifier.scopus | eid_2-s2.0-85062781154 | - |
dc.identifier.hkuros | 304229 | - |
dc.identifier.spage | 290 | - |
dc.identifier.epage | 299 | - |
dc.identifier.isi | WOS:000473277300039 | - |
dc.publisher.place | New York, NY | - |