Conversational Agents and Learning Outcomes: An Experimental Investigation
Bibliographic record
Abstract
Abstract: An experimental approach was used to compare two types of web interfaces (text-based vs. conversation-based) to content on the life and theories of Jean Piaget. The content in each interface was identical with the exception of third- vs. first-person references. Fifty-nine students in psychology first completed a pretest of Piagetian knowledge and then were randomly assigned to one of the two interfaces. After 20 minutes of review/conversation, students completed a 35-item exam designed to measure knowledge retention and a questionnaire to measure their perceptions of the assigned interface. Contrary to expectations, the text-based interface was rated significantly higher on measures of enjoyment and utility and led to better learning outcomes in comparison to the conversational agent. Altogether, the findings indicate that the use of conversational agents in distance education needs to be carefully matched to the learning goals and outcomes. The use of conversational agents in distance education falls under the broader category of pedagogical agents, or the design of computer software that is autonomous, interactive, anthropomorphized, and directed towards educational goals and outcomes The design of pedagogical agents is guided by a number of theoretical frameworks drawn from different disciplines. For example, the work of Graesser and colleagues on AutoTutor, a conversational intelligent tutor system (see Graesser, Wiemer-Hastings, Wiemer-Hastings, Kreuz, & Tutoring Research Group 1999), is based
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".