Commentary: Learning analytics: Ephemeral rhetoric or valuable approach?
Bibliographic record
Abstract
A recent education conference made me aware of the popularity and pervasiveness of the term “learning analytics,” particularly a presentation by Abelardo Pardo who is an executive member of the Society for Learning Analytics Research (SoLAR, www.solaresearch.org). SoLAR promotes the role of analytics in the future of learning and defines learning analytics as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” The purpose of learning analytics is simple to comprehend. However, the language of those promoting the pursuit is often convoluted and verging on shallow pretention. The first Learning Analytics and Knowledge Conference in Banff Canada in 2011 was promoted with the introduction “The growth of data surpasses the ability of organizations to make sense of it. This concern is particularly pronounced in relation to knowledge, teaching, and learning. Learning institutions and corporations make little use of the data learners throw off in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at course and institutions levels. Corporations face pressure for increased competitiveness and productivity, a challenge that requires important contributions in organizational capacity building from work place and informal learning. Learning analytics can play a role in highlighting the development of employees through their learning activities.” 1 It is patronising to suggest that individuals, faculties, and institutions have not analyzed what and how they have been teaching until 2011. Of course, any activity can be brought to a higher level and there would be general agreement that we should learn from analyses of what we have been doing. The emergence of the umbrella-title learning analytics has also created a semantic battle in terminology with the more established field of educational data mining 2. This demarcation dispute does little credit to those involved who seem more interested in defining their specialty than making constructive progress in teaching. Although the term learning analytics has great cache as a current popular banner title, I despair that the peak body for its promotion promulgates the activity with shallow jargon like what follows. “In enterprise settings, information flow and social interactions can yield novel insights into organizational effectiveness and capacity to address new challenges or adapt rapidly when unanticipated event arise. As we witness the expansion of learning and knowledge work beyond formal institutional boundaries, myriad platforms in the cloud hosting the activity of individuals will be providing/exchanging analytics. Advances in knowledge modeling and representation, the semantic web, data mining, analytics, and open data form a foundation for new models of knowledge development and analysis. The technical complexity of this nascent field is paralleled by a transition within the full spectrum of learning (education, work place learning, informal learning) to social, networked learning. These technical, pedagogical, and social domains must be brought into dialogue with each other to ensure that interventions and organizational systems serve the needs of all stakeholders.” 1 Core activities that are associated with learning analytics include statistics, business intelligence, web analytics, operational research, artificial intelligence, and data mining 3. For many, the term learning analytics is replacing the term “evaluation.” The purpose of this commentary has been to draw attention to the currency of the term learning analytics and show the potential for this title to enter the educational lexicon as an established part of good teaching practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".