Students' perceptions of learning affordances, impacts and challenges of blended language learning
Bibliographic record
Abstract
Blended language learning, the integration of technological tools into physical classroom teaching, has gained increasing significance, especially in higher education.Although blended learning (BL) is favoured by many higher educational institutions, past research examining the benefits of blended learning showed varied results.Despite the development of different technologies, the effectiveness of BL has not been enhanced over the decades.Drawing from the discovered issues, research called for the contributions of technology in different learning conditions to seek an optimal approach of applying technology into face-to-face teaching in BL courses.The present study applied Interaction Hypothesis, Sociocultural theory and Constructive theory as a theoretical framework to investigate learners' perceptions of the BL environment including learning affordances and impacts of blended language learning as well as challenges they have encountered.A mixed research approach consisting of an online questionnaire and interviews were employed.A total of 30 English language learners from the School of Continuing Studies of a major university in Canada participated in the study, among which eight language learners with different backgrounds were interviewed.Results showed students' positive perceptions of BL course in terms of its learning affordances of effectiveness, flexibility, increased collaborative work opportunities.However, challenges, namely the lack of online system training, non-interactive online exercises and isolation of web-based learning from classroom learning were detected.To maximize the learning effectiveness and learners' satisfaction, it is suggested to (1) provide students with sufficient technical training and support (2) design more engaging online activities.Also, it is strongly suggested to provide teacher professional training on the effective usage of educational technology in BL environment.iii Résumé L'apprentissage hybride (Blended Learning), c'est-à-dire l'intégration d'outils technologiques dans l'enseignement en classe, a acquis une importance croissance dans l'enseignement des langues, en particulier dans l'enseignement supérieur.Bien que l'apprentissage hybride (BL) soit préféré par de nombreux établissements d'enseignement supérieur, des recherches antérieures portant sur les avantages de l'apprentissage hybride ont montré des résultats variés.Malgré le développement différentes technologies, l'efficacité de la BL n'a pas été améliorée au cours des décennies.S'appuyant sur les problèmes découverts, la recherche a appelé à la contribution de la technologie dans différentes conditions d'apprentissage afin de proposer une approche optimale pour appliquer la technologie à un enseignement en présentiel dans des cours de BL.La présente étude a appliqué l'hypothèse d'interaction, la théorie socioculturelle et la théorie constructive en tant que cadre théorique permettant d'étudier les perceptions des apprenants sur l'environnement BL, y compris les avantages de l'apprentissage et les impacts de l'apprentissage hybride des langues, ainsi que les difficultés rencontrées.Une approche de recherche mixte a été utilisée : elle consistait en un questionnaire en ligne et des entretiens.Au total, 30 apprenants de langue anglaise de l'École d'éducation permanente d'une grande université canadienne ont participé à l'étude, parmi lesquels huit apprenants de langues d'origines différentes ont été interviewés.Les résultats ont montré que les étudiants avaient une perception positive du cours utilisant la BL, car cela confère efficacité, souplesse et possibilités de travail en collaboration accrues.Toutefois, des problèmes ont été détectés, à savoir le manque de formation à propos du système en ligne, d'exercices en ligne non interactifs et de l'isolement de l'apprentissage en ligne versus l'apprentissage en classe.Pour maximiser l'efficacité de l'apprentissage et la satisfaction des apprenants, il est suggéré (1) de fournir aux étudiants une formation et un soutien techniques suffisants (2) afin de concevoir des activités en ligne plus engageantes.De plus, il est fortement suggéré de (3) dispenser une formation professionnelle aux enseignants sur l'utilisation efficace des technologies éducatives dans l'environnement BL.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".