Learning Analytics

Use and impact of analytics

(1) Improving the quality of teaching

Analytics have been used to improve teaching:-

Analytics can be used by lecturers and tutors to monitor the performance of their students while the module is taking place; they can then adapt their teaching if, for example, they identify that students are struggling with a particular topic.

Learning can furnish teaching staff with better information on the quality of the educational content and activities they are providing, and on their teaching and assessment processes, to enable its continual enhancement

(2) Boosting retention

A better understanding of data about learners and their learning can help universities to tackle high attrition rates, which result in adverse impacts on the lives of those affected, and wasted expense for the institutions.

Analysis of data from Nottingham Trent University showed that less than a quarter of students with a low average engagement score progressed from the first to the second year, whereas over 90% of students with good or high average engagement scores did so.

(3) Enabling students to take control of their own learning

Giving students better information on how they are progressing and what they need to do to meet their educational goals.

Some universities are providing analytics- based systems to help students to provide optimum pathways through their studies, by the means of:

  • select future modules,
  • building on data about their career choices,
  • aptitudes and
  • grades for previous modules

4) Analytics have been perceived positively by students

Learning analytics can provide students with an opportunity to take control of their own learning, give them a better idea of their current performance in real-time and help them to make informed choices about what to study.

5) analysis of the strengths, weaknesses, opportunities and threats identified in the literature.

  • Strengths include large volumes of available educational data, the ability to use powerful, pre-existing algorithms, the availability of multiple visualisations for staff and students, increasingly precise models for adaptation and personalisation of learning, and growing insight into learning strategies and behaviours
  • Weaknesses are the potential misinterpretation of the data, a lack of coherence in the sheer variety of data sources, a lack of significant results from qualitative research, overly complex systems and information overload
  • Opportunities include using open linked data to help increase compatibility  across systems, improving self-reflection, self-awareness and learning through intelligent systems, and the feeding of results to other systems to help decision making
  • Threats discussed are ethical and data privacy issues, “over-analysis” and the lack of generalisability of the results, possibilities for misclassification of patterns, and contradictory findings.



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