PROBLEM-BASED LEARNING IN A LARGE CLASSROOM SETTING: METHODOLOGY, STUDENT PERCEPTION AND PROBLEM-SOLVING SKILLS
Bibliographic record
Abstract
Problem-based learning (PBL) can be described as a learning environment where the problem drives the learning. Students are given a problem that is posed such that they realize the need to gain up to date, evidence-based knowledge before they can solve the problem. This drives the students to investigate and discuss identified learning issues in groups with the instructor as facilitator and coach. The following immediate benefits to students have been identified: increased retention of information; an integrated (rather than discipline-bound) knowledge base; development of lifelong learning skills; exposure to real-life experience at an earlier stage in the curriculum; increased student-faculty liaison; and an increase in overall motivation (Greening, 1998). These advantages of PBL could stem from the fact that this process is based on several modern insights on learning, including constructive, selfdirected, collaborative and contextual learning. It will be demonstrated how a PBL approach has been used in the University of British Columbia Okanagan 3rd and 4th year undergraduate biology and biochemistry classes of 50 85 students, although this instructional methodology is not limited to life sciences and can be used in other disciplines. Problems are presented and solved through group discussion and independent study without the need for additional tutors. This technique was introduced to enhance the learning experience and effectiveness by supplementing standard lecture material with a novel, interactive course delivery technique. It is becoming evident that PBL in a small group setting has a robust positive effect on student learning and skills. PBL studies develop student research and independent problem-solving skills. They also challenge students, show them the relevance of the material they are studying, and emphasize the benefits and importance of teamwork and effective communication. However, very little research has been done on the educational benefits of PBL in a large classroom setting. Furthermore, several studies have suggested that PBL may not be superior to conventional educational approaches in all aspects of learning. Therefore, it cannot be assumed that introducing the PBL technique to a large undergraduate class setting will lead to enhanced student learning as well as satisfaction. The superiority, or at least the non-inferiority, of PBL over the standard course delivery techniques must be proven for each individual PBL delivery method. We are therefore exploring various approaches that could be used to compare student learning during PBL exercises and standard didactic lectures, and to assess student perception of this process. We have performed a study that shows that student problem-solving skills are improved after they are exposed to PBL exercises in a large classroom setting. By using student surveys and other techniques, we have also identified a number of parameters that show increased student engagement and satisfaction during the PBL exercises compared to standard didactic lectures. Future studies aimed at assessing student learning during the large class PBL exercises will also be discussed. This research is needed to justify further implementation of PBL techniques in courses that are delivered to large undergraduate classes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".