Learning methods
المؤلف:
Rob Cowdroy & Anthony Williams
المصدر:
Enhancing Teaching and Learning through Assessment
الجزء والصفحة:
P92-C9
2025-06-15
711
Learning methods
Increasing emphasis on both facilitative thinking ability and thinking task ability (discussed above) requires that multiple levels of thinking ability are developed in each student, requiring multiple learning methods (Gibbs, 1995). Lower-level task abilities represent foundation abilities within any given program, and typically require linear thinking and relatively simple behavioral responses (e.g. processing data, answering questions, writing synoptic reports, making analogue (e.g. numerical) tables, graphics and models). Appropriate learning methods for lower-level task ability include conventional rote, recognition and repetition (RRR) methods.
Mid-level task abilities broadly represent the supporting diagnostic and problem-solving abilities in a practitioner, and typically involve lateral thinking to relate observed phenomena to set knowledge and abstract theoretical frameworks. These are more complex behavioral responses, including domain specific dialectic, diagnosis and debate (DDD). This requires a significant shift towards student-centred heuristic learning (learning by individual searching and experimenting). Case-study based cognitive apprenticeship learning strategies, for instance, have been very successful in developing mid-level task abilities.
Higher-level task abilities are typically technical and professional "expertise" abilities (e.g. complex creative architectural design; complex medical prognosis) and involve significantly more complex behavior including "professional" approaches to prognostic/design projections, multiple alternative options, and development of criteria for selection of a "best" complex outcome and "best" complex strategy for achieving that. Effective learning strategies for higher-level task abilities are characteristically heuristic and increasingly research-and-development (R&D) oriented (i.e. closely related to the way an expert practices) with extensive praxis, often in simulated practice environments and some problem-based learning, integrated-learning and research-based learning approaches.
A further important consideration is that individual students, in addition to having differing learning abilities also learn at differing rates: our research shows that within any class or cohort, a majority can be expected to be "good to excellent", a minority can be expected to be "struggling", and a further small minority can be expected to be "outstanding". For present discussion, "outstanding" students are those few who exhibit exceptional ability akin to genius that transcends the expected outcomes.
"Struggling" students typically exhibit high early rates of progress (learning curve) until they reach their (low) ability plateau, and then exhibit little further progress; "good to excellent" students typically exhibit a high early rate of progress, an extended plateau, and a further high rate of progress approaching a deadline; "outstanding" students typically exhibit a low rate of progress (an extended low plateau) until close to a deadline and then an exceptionally high rate of progress.
Ideally, in order to accommodate the struggling students, good to excellent students and outstanding students equitably, differing rates of learning should be accommodated within an overall learning strategy, such as through flexible timetabling and flexible criteria frameworks (Cowdroy & Mauffette, 2003; Crick & Cowdroy, 1999).
In any given course or program that is intended to develop higher-level task abilities, therefore, a combination of learning strategies such as RRR, DDD and R&D should be engaged, in conjunction with flexible timetabling and flexible criteria frameworks to accommodate students' varying learning patterns.
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