Table 2.
First author, year | Clinical topic(s) | Theoretical framework(s) | Platform | Adaptivity subdomains | ||||
Adaptivity method | Adaptivity goals | Adaptivity timing | Adaptivity factors | Adaptivity types | ||||
Casebeer, 2003 | Chlamydia screening | Transtheoretical model of change, problem-based learning, situated learning theory | NR | Designed adaptivity | To increase learning effectiveness (knowledge, skills). | Throughout the training, after case-based and practice-based questions. | User answers to questions |
|
Cook, 2008 |
Diabetes, hyperlipidaemia, asthma, depression | NR | NR | Designed adaptivity | To increase learning efficiency (knowledge gain divided by learning time). | After each case-based question in each module (17 to 21 times/module). | User knowledge |
|
Crowley, 2007 | Dermopathology, subepidermal vesicular dermatitis | Cognitive tutoring | SlideTutor | Algorithmic adaptivity | To increase learning gains, metacognitive gains and diagnostic performance. | At the beginning of each case. | User actions: results of problem-solving tasks; requests for help |
|
Crowley, 2010 | Dermopathology, melanoma | Cognitive tutoring | SlideTutor | Algorithmic adaptivity | To improve reporting performance and diagnostic accuracy. | At the beginning of each case. | User actions: results of problem-solving tasks; reporting tasks; requests for help |
|
de Ruijter, 2018 | Smoking cessation counselling | I-Change Model | Computer-tailored e-learning programme | Designed adaptivity | To modify behavioural predictors and behaviour. | At the beginning of the training. | Demographics, behavioural predictors, behaviour |
|
El Saadawi, 2008 | Dermopathology, melanoma | Cognitive tutoring | Report tutor | Algorithmic adaptivity | To teach how to correctly identify and document all relevant prognostic factors in the diagnostic report. | At the beginning of each case. | User actions, report features |
|
El Saadawi, 2010 | Dermopathology | Cognitive tutoring | SlideTutor | Algorithmic adaptivity | To facilitate transfer of performance gains to real world tasks that do not provide direct feedback on intermediate steps. | During intermediate problem-solving steps. | User actions: results of problem-solving tasks; reporting tasks; requests for help |
|
Feyzi-Begnagh, 2014 | Dermopathology, nodular and diffuse dermatitis | Cognitive tutoring, theories of self-regulated learning | SlideTutor | Algorithmic adaptivity | To improve metacognitive and learning gains during problem-solving. | During each case or immediately after each case. | User actions: results of problem-solving tasks; reporting tasks; requests for help |
|
Hayes-Roth, 2010 | Brief intervention training in alcohol abuse | Guided mastery | STAR workshop | NR | To improve attitudes and skills. | During clinical cases. | User scores, user-generated dialogue |
|
Lee, 2017 |
Treatment of atrial fibrillation | NR | Learning assessment platform | Designed adaptivity | To increase learning effectiveness (knowledge, competence, confidence and practice). | After learning gaps identified in the first session. | Learning gaps in relation to objectives |
|
Micheel, 2017 | Oncology | Learning style frameworks | Learning-style tailored educational platform | Designed adaptivity | To increase learning effectiveness (knowledge). | After assessing the learning style. | Learning style |
|
Morente, 2013 | Pressure ulcer evaluation | NR | ePULab | Designed adaptivity | To increase learning effectiveness (knowledge, skills). | Each pressure ulcer evaluation. | User skills |
|
Munoz, 2010 | Management of childhood illness | Learning styles framework | SIAS-ITS | Designed adaptivity | To increase learning effectiveness and efficiency. | At the beginning of the training. | User knowledge, user learning style |
|
Romito, 2016 | Transoesophageal echocardiography | Perceptual learning | TOE PALM | Algorithmic adaptivity | To improve response accuracy and response time. | After each clinical case. | User response accuracy, user response time |
|
Samulski, 2017 | Cytopathology, pap test, squamous lesions, glandular lesions | NR | Smart Sparrow |
Designed adaptivity | To improve learning effectiveness. | During intermediate problem-solving steps. | User knowledge |
|
Thai, 2015 |
Electrocardiography | Perceptual learning theory, adaptive response-time-based algorithm | PALM | Algorithmic adaptivity | To improve perceptual classification learning effectiveness and efficiency. | After each user response. | User response accuracy, user response time |
|
Van Es, 2015 | Diagnostic cytopathology, gynaecology, fine needle aspiration, exfoliative fluid | NR | Smart Sparrow |
Designed adaptivity | To improve learning effectiveness. | During intermediate problem-solving steps. | User responses |
|
Van Es, 2016 | Diagnostic cytopathology,; gynaecology, fine needle aspiration, exfoliative fluid | NR | Smart Sparrow |
Designed adaptivity | To improve learning effectiveness. | During intermediate problem-solving steps. | User responses |
|
Wong, 2015 | Diagnostic imaging, chest X-rays, computed tomography scans | Cognitive load theory | Smart Sparrow |
Designed adaptivity | To improve learning effectiveness. | During intermediate problem-solving steps. | User responses |
|
Wong, 2017 | Fetal heart rate interpretation | Perceptual learning | PALM | Algorithmic adaptivity | To improve response accuracy and response time. | After each clinical case. | User response accuracy, user response time |
|
Woo, 2006 | Haemodynamics, baroreceptor reflex | NR | CIRCSIM tutor | Algorithmic adaptivity | To improve knowledge related to problem-solving tasks. | After each user response. | User knowledge, user responses |
|
ePULab, electronic pressure ulcer lab; NR, not reported; PALM, perceptual adaptive learning module.; SIAS-ITS, SIAS intelligent tutoring system; TOE PALM, transoesophageal echocardiography perceptual adaptive learning module.