{"id":"W3110578927","doi":"10.19173/irrodl.v21i4.4834","title":"Mobile Technology Acceptance Scale for Learning Mathematics: Development, Validity, and Reliability Studies","year":2020,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Likert scale; Scale (ratio); Reliability (semiconductor); Nomological network; Discriminant validity; Content validity; Validity; Psychology; Confirmatory factor analysis; Convergent validity; Computer science; Mathematics education; Mathematics; Statistics; Psychometrics; Internal consistency; Structural equation modeling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01110451,0.00009006493,0.0003675748,0.0001591576,0.0002836125,0.0001341978,0.001375234,0.00006320589,0.00007751027],"category_scores_gemma":[0.02733262,0.0000579428,0.00003427627,0.0008204741,0.0004721975,0.0001916631,0.00179103,0.0006241731,0.00001398525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004925028,"about_ca_system_score_gemma":0.00008950236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005201482,"about_ca_topic_score_gemma":0.000005197426,"domain_scores_codex":[0.9977942,0.0003394639,0.0006211078,0.0003696113,0.0006863918,0.000189235],"domain_scores_gemma":[0.9965106,0.00213426,0.0002461484,0.0001964898,0.0008580486,0.0000544869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001315327,0.0001685169,0.8372722,0.001521506,0.00003972217,0.000006190872,0.002348957,0.0001561862,0.0002278315,0.005416556,0.005095694,0.1476151],"study_design_scores_gemma":[0.002243177,0.0008554252,0.05104664,0.007698332,0.00003266225,0.00003945173,0.09397777,0.006405834,0.001620344,0.07548647,0.7601507,0.0004432326],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575012,0.01233767,0.003066888,0.02527784,0.00004738549,0.001241345,0.0000231621,0.00004082633,0.000463656],"genre_scores_gemma":[0.9778647,0.01672609,0.004680593,0.00012418,0.00001337592,0.0002665283,0.00001468162,0.000006320442,0.0003035082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7862256,"threshold_uncertainty_score":0.9808606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3668359242065714,"score_gpt":0.5466350092318268,"score_spread":0.1797990850252554,"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."}}