During this session, participants will conduct analysis of de-identified data from a large, cross-institutional project using an Excel-based Programme Review Tool (PRT) to understand the benefits of using learning analytics (LA) for programme review. Research on applying LA in higher education has mainly focused on academic success and retention (Siemens, Dawson & Lynch, 2014), and predicting student grades or identifying students at risk within a subject (Gašević, Dawson, Rogers & Gasevic, 2016). However, there is little research on using LA for curriculum review at the programme level (Méndez, Ochoa & Chiluiza, 2014; Viberg, Hatakka, Balter & Mavroudi, 2018). The approach presented in this session addresses this gap and the data participants will analyse and discuss illustrates the potential of LA for programme review. During the session we will:
- Introduce the LA approach to programme review, including the guiding framework and provide a brief description of the project currently being conducted;
- Work with participants as they use the Excel tool to analyse de-identified real programme data, particularly student grades according to the P-MAI (Plan-Map, Analyse, Implement) model
- Facilitate a brief discussion of the findings, the participants’ perception of the flexibility of the tool and the value of the approach to complement review processes.
- Provide interested participants with information on how they can access Excel-based tools developed as part of the project to conduct programme reviews using this approach themselves.
This session will be highly interactive, with minimal transmission of information by facilitators. Instead, participants will actively engage with concepts such as the purpose of programme review and how learning analytics can help inform improvement. An innovative data analysis tool developed for the project will be used in the session to analyse a set of data according to an identified model. Outcomes, that is, how results from programme reviews using a learning analytics approach can contribute to quality improvement will be demonstrated. The activities in the session will allow participants to actively construct their understanding of the benefits and challenges of using learning analytics for this purpose. The presenters appreciate that timing may need to be adjusted.
Outline according to the Projects P-MAI model
Introduction. Provide dataset and PRT to participants. Overview of the current project using a learning analytics approach to programme review and the outcomes to date.
- Demonstration: Demonstrate Programme Review Tool (PRT) including reference to review questions (10 minutes)
Using Programme X data, participants undertake analyses based on programme review questions
- The Programme X data is de-identified data drawn from real programme reviews already conducted as part of the presenters project. Participants will split up into small groups of 4-5 to use the PRT to analyse a set of programme data supplied by the presenters. The groups will be given a set of questions to help structure the analyses. These questions will be designed to highlight the relationships between different types of review questions and analyses that can be used to address them (5 minutes)
- Programme overview: Student entry characteristics, Graduation rates, Grades analysis (10 minutes)
- Programme and subject difficulty analysis: Analysis of relationships between subject grades, student satisfaction with subjects and subject difficulty, examples of outcome prediction models (10 minutes)
- Assessing constructive alignment in the curriculum (10 minutes)
- Whole group discussion of analyses, outcomes, potential actions (10 minutes)
- Summary: The presenters will: summarise the preceding discussion and outcomes; invite participants to contact the presenters if they are interested in conducting programme reviews themselves using the tools being developed (5 minutes)
Armatas, C. and Spratt, C. 2018. Evidence, analysis, action: Using learning analytics to direct curriculum review and improve student learning outcomes. In K.
Enomoto, K., Warner, R., and Nygaard, C. eds., Innovative teaching and learning practices in higher education. Farringdon: Libri Publishing, pp.219-235.
Dede, C., Ho, A. and Mitros, P. 2016. Big data analysis in higher education: Promises and pitfalls. Educause Review, [e-journal] 23(4). Available at: https://er.educause.edu/articles/2016/8/big-data-analysis-in-higher-education-promises-and-pitfalls [Accessed 11 March 2019].
Greller, W. and Drachsler, H. 2012. Translating learning into numbers: A generic framework for learning analytics, Educational Technology & Society, 15. pp.42-57. Available at: https://www.researchgate.net/journal/1436-4522_Educational_Technology_Society [Accessed 1 February 2019].
Gašević, D., Dawson, S., Rogers, T., and Gasevic, D. 2016. Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28. pp.68-84. doi.org/10.1016/j.iheduc.2015.10.002.
Liaqat, A, Hatala, M., Gašević, D. and Jovanović, J. 2012. A qualitative evaluation of evolution of a learning analytic tool, Computers & Education, 58. pp.470-489. doi.org/10.1016/j.compedu.2011.08.030.
Liaqat, A, Asadi, M., Gašević, D., Jovanović, J. & Hatala, M. 2013. Factors influencing beliefs for adoption of a learning analytics tool: An empirical study, Computers & Education, 62, pp.130-148. doi.org/10.1016/j.compedu.2012.10.023.
Méndez, G., Ochoa, X. and Chiluiza, K. 2014. Techniques for data-driven curriculum analysis. Paper presented at LAK ’14 Proceedings of the Fourth International Conference on Learning Analytics and Knowledge. Indianapolis, Indiana, USA. doi.org/10.1145/2567574.2567591.
Siemens, G., Dawson, S. and Lynch, G. 2014. Improving the quality and productivity of the higher education sector-Policy and strategy for system-level deployment of learning analytics. Canberra: Australia: Office of Learning and Teaching, Australian Government. Accessed 12 February 2019. http://www.olt.gov.au/system/files/resources/SoLAR_Report_2014.pdf
Viberg, O., Hatakka, M., Bälter, O. and Mavroudi, A. 2018. The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, pp.98-110. doi:10.1016/j.chb.2018.07.027.
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