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Record W7162025284 · doi:10.82308/55538

Students' perceptions of learning affordances, impacts and challenges of blended language learning

2019· dissertation· en· W7162025284 on OpenAlexaboutno aff
Jinxiu Liu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningAffordanceEducational technologyLanguage acquisitionExperiential learningCollaborative learningCooperative learningPerceptionSociocultural evolutionConstructive

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.355
Teacher spread0.339 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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