Incorporating Deep Learning and Higher Order Thinking Skills in a Large, Lecture‐based Human Physiology Course
Bibliographic record
Abstract
Traditionally, didactic lecture methods and multiple‐choice assessments are heavily relied upon in large classes, despite consistently being reported in literature to encourage a surface approach to learning and being limited to assessing lower order (LO) skills (Biggs & Tang, 2011). However, the perceived effort and resources required to restructure an environment such that a deep approach to learning may be encouraged often prevents instructors from altering teaching and assessment methods, particularly in large classes. The goal of this study was to determine if a large lecture‐based course (350+ students) could be modestly, but manageably, modified to support a deep approach to learning and promote the development of higher order (HO) thinking skills. Specifically, this study took place in a two‐semester Human Physiology course sequence (Phys I and Phys II) taught with instructor‐led scaffolded lectures and assessed with long‐answer written tests. It was hypothesized that this teaching and assessment structure would encourage a deep approach to learning and develop students' HO thinking skills. The Revised Two‐Factor Study Process Questionnaire (rSPQ) was administered at the beginning and end of each course to measure student approach to learning score, while student academic performance was tracked on assessment questions categorized as requiring either LO or HO thinking skills, according to the Blooming Biology Tool (BBT). Student performance on HO thinking questions remained consistent from the start to end of Phys 1 (72.9±19.4% versus 74.8±20.7%, p =0.37), but significantly improved over the course of Phys II (69.9±18.4% versus 79.4±14.8%, p <0.001). Unexpectedly, students' performance on LO thinking questions decreased in a similar pattern from the start to end of both Phys 1 (78.5±20.6% versus 69.4±17.9%, p <0.001) and Phys II (80.5±19.6% versus 72.2±24.3%, p <0.001). There was no significant change in deep or surface approach to learning scores over the course of either semester; although students consistently preferred a deep approach to a surface approach at each time point measured. Limitations regarding the tool used to measure approach to learning, combined with specific student, classroom and educational factors, may partially explain the lack of measurable changes. However, these results suggest that a large lecture‐based course which has been modestly, but manageably, modified from traditional teaching and assessment methods, can provide a learning environment which supports the maintenance of a deep approach to learning and promotes the development of HO thinking skills. Biggs JB , Tang CS , Society for Research into Higher Education . Teaching for Quality Learning at University: What the Student Does [Online]. McGraw‐Hill Education. http://search.ebscohost.com.subzero.lib.uoguelph.ca/login.aspx?direct=true&db=nlebk&AN=405333&site=ehost‐live&scope=site [12 Jul. 2018]. Support or Funding Information SSHRC Doctoral Fellowship This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".