Predicting Learners' Emotional Response in Intelligent Distance Learning Systems.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Different research studies have proved that emotions meet a pivotal role in cognitive processes and in particular the studies made by Damasio who argues that human-beings without emotions could not make the simplest decision (Damasio 1994). We think that the fail of Intelligent Distance Learning Systems to achieve an efficient learning is mainly resulting from the lacking of Emotional Intelligence abilities. These systems require a capacity to manage the emotional state of the learner so as to be in the best conditions for learning. To achieve this goal, it is very important to anticipate the emotional response of the learner after the happening of an event in the learning session. In this paper, we propose a method for predicting the learners’ emotional response by using an intelligent agent called ERPA (Emotional Response Predictor Agent). This agent uses a case-based reasoning, an Artificial Intelligence technique, and a Learner’s Event-Appraisal Model.
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.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it