Tag Archives: Intervention

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

This paper reports on the research findings of the Open Academic Analytics Initiative, and features eight findings.

Most positive is the finding that relatively simple intervention strategies designed to alert students early in a course that they may be at risk academically can have a positive impact on student learning outcomes such as overall course grades.

Most concerning is the finding that interventions can have unintended consequences, such as triggering students to withdraw from courses to avoid academic and financial penalties.

Other findings

  1. The feasibility of implementing an opensource earlyalert prototype for higher education, and provides a detailed account of the challenges and design criteria used in implementing such a system.
  2. The strength of scores derived from partial contributions to the student’s final grade as predictors of academic performance.
  3. How these predictive models can help the instructor detect students at academic risk earlier in the semester.
  4. Initial evidence that predictive models can be imported from the academic context in which they were developed to different academic contexts while retaining most of their predictive power.
  5. That there may be benefits associated with customizing imported predictive models using local institutional data as a means to enhance their predictive power further.
  6. That there are no apparent gains between providing students with an online academic support environment and simply making students aware of their potential academic risk.
Citation: http://epress.lib.uts.edu.au/journals/index.php/JLA/issue/view/307 | Url: Jayaprakash, Sandeep M, Moody, Erik W, Lauría, Eitel J M, Regan, James R, & Baron, Joshua D. (2014). Early alert of academically at-risk students: an open source analytics initiative. Journal of Learning Analytics, 1(1), 6-47.

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

This paper describes the results on research work performed by the Open Academic Analytics Initiative, an on-going research project aimed at developing an early detection system of college students at academic risk, using data mining models trained using student personal and demographic data, as well as course management data. It reports initial findings on the predictive performance of those models, their portability across pilot programmes in different institutions and the results of interventions applied on those pilots.

Comment from Martyn Cooper: This paper looks at the integration of demographic information and analytics of online activity as a means of predicting students at risk. Importantly, it looks across different programmes in several institutions. Th authors claim that predictive models are more portable than may have been initially assumed. However, again the study is an initial investigation and only looked at data over one semester. They highlight that some students improve after “treatment” and others do not and this needs further investigation.

Citation: E. J. M. Lauria, E. W. Moody, S. M. Jayaprakash, N. Jonnalagadda, J. D. Baron (2013). Open academic analytics initiative: initial research findings, In: Third International Learning Analytics and Knowledge Conference (LAK13), 8-12 April, Leuven, Belgium. | Url: http://dl.acm.org/citation.cfm?id=2460296