Reducing Teachers' Cognitive Overload with a Recommender System in the Workplace
Bibliographic record
Abstract
This paper presents an assistant, ARIALE (Authoring Resources for Implementing AdaptiveLearning Environments), that helps decrease the teacher's cognitive load when he has to deal with traditional help techniques, complex tasks or unknown environments in the workplace.The assistant adapts learning support and problemsolving support according to teachers' characteristics, while they are authoring learning sessions to teach decision-making in Network Design.ARIALE applies on-the-job training when a teacher can not spend time learning how to use a tool or how to solve a problem.Our methods for problem-solving support include Bayesian learning to recommend network topologies and our assistant provides adapted help to use a tool rather than teach how to use it.In order to reduce teachers' cognitive overload, problem-solving support helps the user in solving a problem related to Network Design.Instead of helping the user to achieve a goal step-by-step, problem-solving support provides the solution to the problem.According to the approach of our system for teaching Network Design, an example is a network topology with different number and types of links.Our system includes an implementation of problemsolving support and recommends topologies that the teacher can include in his teaching, when he does not have enough time to create an example from scratch [16].Learning support is a way to help teachers who do not have enough time to learn the use of a particular tool.Learning support works parallel to the authoring tool, and finds, selects and adapts contextsensitive support for Web-based help, according to a teacher's skills and plans [15].Then, our system selects the help strategy and techniques to deliver support, the kind of help content to show, and the media to display the content adapted to the teacher's preferences.This paper also discusses other characteristics of the help provided by ARIALE and we describe the methods that our system uses to adapt help to the teacher's attributes and plan. Research rationaleThis research addresses two problems that university teachers face when they are authoring teaching materials for their courses:1. Teachers do not have enough time to create teaching materials.2.Teachers do not have enough time to learn how to use the tools that could reduce the time required for the creation of materials.In general, users of software and, particularly, authors of educational material such as teachers face other two difficulties:1.The production paradox that could be experienced by teachers.The teacher is not familiar with the functionalities of the tool he is going to use,
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".