MétaCan
Menu
Back to cohort
Record W4416619368 · doi:10.1108/dl-11-2017-0007

What Is Problem-Based Learning?

2017· article· en· W4416619368 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDistance Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical knowledgeHigher educationMedical schoolProfessional developmentDiscipline

Abstract

fetched live from OpenAlex

There are many strategies instructors can use to engage their learners in meaningful learning. One approach, problem-based learning, has its roots in medical education. It was first introduced in the 1950s at Case Western Reserve University. Faculty preparing doctors needed a way to support students’ ability to apply professional skills and knowledge in real-world contexts. Problem-based learning influenced the instructional approaches and curriculum used in medical schools by challenging medical professionals to help their students apply their content knowledge to real medical cases. This methodology, eventually called “problem-based learning,” was officially adopted as a pedagogical approach at Canada’s McMaster University to promote students’ ability to apply their scientific knowledge to clinical situations (Neufeld & Barrows, 1974). The model spread to academic programs for law, business, and education. Currently problem-based learning is used as the predominant approach to learning at various institutions of higher education around the world including the University of Delaware, Maastricht University in the Netherlands, Gadjah Mada University in Indonesia, and the University of Limerick in Ireland.Problem-based learning (often referred to as “PBL”) is also the name for an established instructional model of teaching that challenges students to learn and apply knowledge of content through the application of problem-solving skills to solve meaningful problems in the academic disciplines (Kilbane & Milman, 2013). It consists of the following four phases:This model is intended to be used by educators at all educational levels and settings to build learners’ problem-solving skills while also solving a problem.Another approach with the same abbreviation, “project-based learning” (also referred to as “PBL”), has also become popular in many educational settings. Projectbased learning is a method to promote students’ engagement in the learning process through the structuring of learning around the accomplishment of projects or tasks that have meaning and relevance for the learner. In this type of learning, students have a great deal of say about the projects they will work on and how they will work on them. Although project-based learning shares much in common with problembased learning, they are two distinct models of learning. In both models, instructors motivate students by centering learning on the accomplishment of a meaningful goal. In problem-based learning, that goal is solving a problem. In project-based learning, the goal is completion of a project. Table 1 compares these two learning models.There are many ways in which online educators can use problem-based learning. It can be employed as a major problem that takes a long period of time for students to solve (e.g., over the course of an entire semester), or it can be used during a shorter period of time (e.g., one lecture). Often PBL is incorporated as a case. There are many case-related resources available online. A key feature of problem-based learning is identifying a “good” problem for learners to solve. Problems are chosen or developed by the instructor to correspond with learning goals and objectives. According to Schmidt, Rotgans, and Yew (2011), good problems have certain characteristics, which are:The use of problem-based learning in online settings provides instructors with an approach to designing instruction that provides learners with authentic, real- world learning experiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.332
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it