Bibliographic record
Abstract
In the last three decades, information and communications technology (ICT) have evolved very rapidly.Among other things, it resulted in new opportunities in course delivery modes: Online and blended courses (Siemens et al., 2015).A course delivery mode refers to the decisions about the way teaching, learning, and assessment activities are conveyed to students.Thanks to ICT, these delivery modes offer advantages both to students and to postsecondary institutions.On the one hand, depending on their design, blended and online courses have the potential to meet the demands of students who desire flexible course schedules, such as adult students, because of family and/or work responsibilities (Lakhal, 2019).They also give students better access to higher education, especially in large countries such as Canada where distances can be quite significant between students' homes and their colleges or universities (Lakhal et al., 2020), and they allow both synchronous and asynchronous contacts between students and with the instructor, even if students would not need to travel as much to attend face-to-face sessions (Cidral et al., 2018).On the other hand, online and blended courses provide colleges and universities with some financial benefits (Gosmire et al., 2013), as they can free up space on campuses, since students and instructors no longer have to meet face-to-face in a classroom for each class session.Moreover, as some meta-analyses (Bernard et al., 2004(Bernard et al., , 2014;;Means et al., 2013) have revealed, blended and online courses appear to be at least as effective for student learning as face-to-face courses delivered on campus.A number of models have been proposed to categorize the modes by which courses can be delivered.Most of them oppose the proportion of courses taught face-to-face to those mediated by online technologies (Graham, 2013).As reported by Graham (2013), other authors have used different criteria as the basis of their model.For example, from formal to informal learning, or as a continuum from face-to-face instructor-led learning to real-time workplace learning supported by online technologies.These models, however, are better suited for corporate training.In higher education, HySup, an often cited European study, classified course delivery modes based on their educational, organizational, and material characteristics, resulting in six types of courses (Burton et al., 2011). Margulieux et al. (2016) classified eight course delivery modes using two criteria for their matrix: The proportion of instruction delivered face-to-face or by online technologies and the instructional approach used in the course, instructor-focused or student-focused.Many colleges and universities also developed their own institutional model of course delivery modes.For example, Mount Saint Vincent University proposes the following categories of courses 1 : Face-toface, blended, multi-access, synchronous online, asynchronous online, and multimode online.Université Laval has recently renewed its categories of courses 2 as follows: Faceto-face, blended classroom, blended distance, distance asynchronous, distance synchronous, and blended synchronous courses.Continuums have been suggested to classify different course delivery modes.The first one, proposed by Allen and Seaman (2007) and lately by Allen et al. (2016), placed 1 https://www.msvu.ca/academics/teaching-and-learning-centre-and-online-learning/take-onlinecourses/course-delivery-modes 2 https://www.ulaval.ca/les-etudes/formules-denseignement
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 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".