{"id":"W4413070981","doi":"10.1007/978-3-031-98414-3_19","title":"Prompts Eliciting Active and Constructive Engagement Improve Learning Across ChatGPT and Traditional Resource Contexts","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Constructive; Computer science; Resource (disambiguation); Artificial intelligence; Knowledge management; Human–computer interaction; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001501991,0.0005050116,0.0005922754,0.000380608,0.000498077,0.001509619,0.0007309752,0.000825659,0.01203132],"category_scores_gemma":[0.01429092,0.0002868382,0.0002203749,0.0003536799,0.0003773211,0.001339507,0.002643052,0.001077072,0.002327448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003114871,"about_ca_system_score_gemma":0.0008014426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005326094,"about_ca_topic_score_gemma":0.001544798,"domain_scores_codex":[0.9984754,0.0006198802,0.00009061011,0.0003119528,0.0003474855,0.0001547813],"domain_scores_gemma":[0.9927413,0.005502956,0.0003974672,0.0004955712,0.0003839943,0.0004785902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008114022,0.005456601,0.008718959,0.003303841,0.00007863314,0.0004084549,0.01196479,0.002726034,0.2813894,0.002655644,0.008358845,0.6668249],"study_design_scores_gemma":[0.005744527,0.03883412,0.2344119,0.003866815,0.001450967,0.001546259,0.03134253,0.06488518,0.481052,0.04606868,0.0902964,0.0005006787],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9188339,0.00071868,0.05665129,0.0005447153,0.0004580024,0.001093521,0.0005448732,0.002976219,0.01817887],"genre_scores_gemma":[0.9400914,0.0003249654,0.0487652,0.0003063369,0.00006994238,0.00113627,0.0004581991,0.0003590031,0.008488664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01203132,"threshold_uncertainty_score":0.04024875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460259942015744,"score_gpt":0.2665489314719274,"score_spread":0.2519463320517699,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}