Adding dispositions to create pedagogy-based Learning Analytics
This empirical study aims to demonstrate how Dispositional Learning Analytics (DLA) can provide a strong connection between Learning Analytics (LA) and pedagogy. Where LA based models typically do well in predicting course performance or student drop-out, they lack actionable data in order to easil...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
Forum Neue Medien in der Lehre Austria
2017-03-01
|
| Series: | Zeitschrift für Hochschulentwicklung |
| Online Access: | https://www.zfhe.at/index.php/zfhe/article/view/993 |
| _version_ | 1827823912113668096 |
|---|---|
| author | Dirk Tempelaar Bart Rienties Quan Nguyen |
| author_facet | Dirk Tempelaar Bart Rienties Quan Nguyen |
| author_sort | Dirk Tempelaar |
| collection | DOAJ |
| description |
This empirical study aims to demonstrate how Dispositional Learning Analytics (DLA) can provide a strong connection between Learning Analytics (LA) and pedagogy. Where LA based models typically do well in predicting course performance or student drop-out, they lack actionable data in order to easily connect model predictions with educational interventions. Using a showcase based on learning processes of 1080 students in a blended introductory quantitative course, we analysed the use of worked-out examples by students. Our method is to combine demographic and trace data from learning-management systems with self-reports of several contemporary social-cognitive theories. Students differ not only in the intensity of using worked-out examples but also in how they positioned that usage in their learning cycle. These differences could be described both in terms of differences measured by LA trace variables and by differences in students’ learning dispositions. We conjecture that using learning dispositions with trace data has significant advantages for understanding student’s learning behaviours. Rather than focusing on low user engagement, lessons learned from LA applications should focus on potential causes of suboptimal learning, such as applying ineffective learning strategies.
29.03.2017 | Dirk Tempelaar, Bart Rienties & Quan Nguyen
|
| first_indexed | 2024-03-12T02:18:32Z |
| format | Article |
| id | doaj.art-c824569d876241e983e861d3fbb1fbb9 |
| institution | Directory Open Access Journal |
| issn | 2219-6994 |
| language | deu |
| last_indexed | 2024-03-12T02:18:32Z |
| publishDate | 2017-03-01 |
| publisher | Forum Neue Medien in der Lehre Austria |
| record_format | Article |
| series | Zeitschrift für Hochschulentwicklung |
| spelling | doaj.art-c824569d876241e983e861d3fbb1fbb92023-09-06T07:20:31ZdeuForum Neue Medien in der Lehre AustriaZeitschrift für Hochschulentwicklung2219-69942017-03-0110.3217/zfhe-12-01/02Adding dispositions to create pedagogy-based Learning AnalyticsDirk Tempelaar0Bart Rienties1Quan Nguyen2Maastricht UniversityOpen UniversityMaastricht University This empirical study aims to demonstrate how Dispositional Learning Analytics (DLA) can provide a strong connection between Learning Analytics (LA) and pedagogy. Where LA based models typically do well in predicting course performance or student drop-out, they lack actionable data in order to easily connect model predictions with educational interventions. Using a showcase based on learning processes of 1080 students in a blended introductory quantitative course, we analysed the use of worked-out examples by students. Our method is to combine demographic and trace data from learning-management systems with self-reports of several contemporary social-cognitive theories. Students differ not only in the intensity of using worked-out examples but also in how they positioned that usage in their learning cycle. These differences could be described both in terms of differences measured by LA trace variables and by differences in students’ learning dispositions. We conjecture that using learning dispositions with trace data has significant advantages for understanding student’s learning behaviours. Rather than focusing on low user engagement, lessons learned from LA applications should focus on potential causes of suboptimal learning, such as applying ineffective learning strategies. 29.03.2017 | Dirk Tempelaar, Bart Rienties & Quan Nguyen https://www.zfhe.at/index.php/zfhe/article/view/993 |
| spellingShingle | Dirk Tempelaar Bart Rienties Quan Nguyen Adding dispositions to create pedagogy-based Learning Analytics Zeitschrift für Hochschulentwicklung |
| title | Adding dispositions to create pedagogy-based Learning Analytics |
| title_full | Adding dispositions to create pedagogy-based Learning Analytics |
| title_fullStr | Adding dispositions to create pedagogy-based Learning Analytics |
| title_full_unstemmed | Adding dispositions to create pedagogy-based Learning Analytics |
| title_short | Adding dispositions to create pedagogy-based Learning Analytics |
| title_sort | adding dispositions to create pedagogy based learning analytics |
| url | https://www.zfhe.at/index.php/zfhe/article/view/993 |
| work_keys_str_mv | AT dirktempelaar addingdispositionstocreatepedagogybasedlearninganalytics AT bartrienties addingdispositionstocreatepedagogybasedlearninganalytics AT quannguyen addingdispositionstocreatepedagogybasedlearninganalytics |