- Title
- Design, implementation and evaluation of a learning analytics intervention framework
- Creator
- Alalawi, Khalid
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2024
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- A focus area in educational data mining (EDM) is predicting student academic performance, which can assist in early warnings for at-risk students, allowing for appropriate interventions. Learning analytics intervention (LAI) is a field that acts upon student performance prediction results to provide intervention and feedback to at-risk students to improve student outcomes. LAI studies have shown positive results with the potential to have significant impact. The literature review and analysis in this thesis discovered that LAIs are yet to be adopted in mainstream education. There are several factors impeding its potential adoption, including the requirement for substantial investments in data collection and the development of institution-specific predictive models. The success of LAIs is also contingent on academic’s ability to provide effective interventions. Also a continuous improvement process model for LAIs is lacking. This thesis focused on addressing these challenges by researching, developing and evaluating a web-based LAI framework, termed Student Performance Prediction and Action (SPPA) framework. Using SPPA, academics can pilot LAIs in their courses seamlessly, without the need for an extensive-level of institution-wide investment. SPPA uses EDM to create course-specific predictive models using continuous assessment data. Learning analytics dashboards (LADs) disseminate students’ risk levels to stakeholders facilitating instructor-led interventions. The framework facilitates effective interventions incorporating sound pedagogical approaches to identify gaps in students’ knowledge, provide personalised study plans and feedback reports, and identify areas where students find challenging. Academics can use well-known tools for personalised communication during interventions. The proposed framework considers course evaluations and multiple course iterations to facilitate a continuous improvement process model in LAIs. The framework was evaluated in a number of real-world educational settings. The evaluations demonstrated SPPA’s potential to improve educational outcomes, enable academics to pilot LAIs in courses seamlessly and garner positive uptake from academics. The SPPA has the potential to catalyse wide adoption in LAIs.
- Subject
- learning analytics interventions; student performance prediction; predicted at-risk students; pedagogical approaches
- Identifier
- http://hdl.handle.net/1959.13/1506608
- Identifier
- uon:55910
- Rights
- Copyright 2024 Khalid Alalawi
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 4 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 536 KB | Adobe Acrobat PDF | View Details Download |