A Case Study of the Introductory Psychology Blended Learning Model at McMaster University
Bibliographic record
Abstract
This paper provides a brief review of blended learning as a didactic method, and discusses the issues and challenges of using blended learning models in post-secondary education. Blended learning refers to mixed modes of instruction that combine traditional face-to-face classroom teaching methods and online learning materials. The paper will address challenges faced by large classrooms with a diverse student body, and the ways blended learning models can help alleviate those concerns (i.e. technologically savvy students, the need for course scheduling flexibility). In addition, a case study of blended learning in higher education in the context of a unique first year Introductory Psychology program at McMaster University will be discussed. Lastly, the important learning benefits offered by blended learning systems, along with the potential barriers to their implementation will be addressed. Cet article présente un bref compte rendu de l’apprentissage hybride en tant que méthode didactique. Il traite des problèmes et des enjeux relatifs à l’utilisation des modèles d’apprentissage hybride dans le domaine de l’enseignement postsecondaire. L’apprentissage hybride renvoie aux modes d’enseignement mixtes qui combinent les méthodes d’enseignement traditionnel en présentiel et l’accès à des documents d’apprentissage en ligne. L’article traite des difficultés rencontrées dans les grands groupes comprenant une diversité d’étudiants et des façons dont les modèles d’apprentissage hybride peuvent contribuer à atténuer ces préoccupations (c.-à-d. les étudiants calés en technologie, la nécessité d’une offre de cours souple). De plus, l’article traite d’une étude de cas sur l’apprentissage hybride dans l’enseignement supérieur dans le cadre de la première année d’un programme d’introduction à la psychologie à l’Université McMaster. Enfin, l’article aborde les importants avantages offerts par les systèmes d’apprentissage hybride ainsi que les obstacles potentiels à leur mise en œuvre.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".