Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems
Bibliographic record
Abstract
Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student’s request. The Help Tutor was a tutor agent that gave in-context, real-time feedback on students’ help-seeking behavior, as they were learning with an ITS. Key goals were to help students become better self-regulated learners and help them achieve better domain-level learning outcomes. In a classroom study, feedback on help seeking helped students to use on-demand help more deliberately, even after the feedback was no longer given, but not to achieve better learning outcomes. The work made a number of contributions, including the creation of a knowledge-engineered, rule-based, executable model of help seeking that can drive tutoring. We review these contributions from a contemporary perspective, with a theoretical analysis, a review of recent empirical literature on help seeking with ITSs, and methodological suggestions. Although we do not view on-demand, principle-based help during tutored problem solving as being as important as we once did, we still view it as helpful under certain circumstances, and recommend that it be included in ITSs. We view the goal of helping students become better self-regulated learners as one of the grand challenges in ITSs research today.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".