Cited by 33English Language Learning and TeachingEFL/ESL Teaching and LearningMathematics Education and Teaching Techniques

A lesson study on a MOOC-based and AI-powered flipped teaching and assessment of EFL writing model: teachers' and students' growth

Zhang, Yumei·2024·International Journal for Lesson & Learning Studies

Abstract

Purpose: The lesson study aims to examine college English teachers' growth in technological pedagogical content knowledge (TPACK) and the improvement of students' learning outcomes in the context of MOOC-based and AI-powered flipped teaching and assessment of EFL writing (MAFTA). Design/methodology/approach: Three college EFL teachers and their students (66 in total) participated in three cycles of MAFTA instruction. Triangulated analysis was conducted by considering all relevant data sources, including the teachers' discussions and reflections, the argumentative essays produced by the students before and after the MAFTA instruction, as well as the data gathered through questionnaires and interviews. Findings: The three teachers demonstrated varying degrees of growth in TPACK, as evidenced by their increased knowledge of the technology tools and skills in utilizing the tools to realize their pedagogical beliefs on teaching EFL writing. Substantial improvements were detected in students' essays. The students generally have affirmative perceptions on the MAFTA model and the questionnaire and interviews specified the benefits they gained from each stage of the model. Originality/value: Firstly, the lesson study is grounded in an innovative approach to teaching EFL writing that incorporates multiple technological affordances. Secondly, it closely scrutinizes the dynamics of both teachers' and students' growths during the innovative practice. The findings could offer insights into teachers' TPACK development and effective integration of technological advancements in EFL education at the tertiary level.

Integrity signals

Registry

Crossref · 10.1108/ijlls-07-2023-0085

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • One study (n = 1) aimed to improve teaching and learning through a focus on AI-assisted flipped instruction and its effect on teacher TPACK and student writing outcomes.

    “One study (n = 1) aimed to improve teaching and learning through a focus on AI-assisted flipped instruction and its effect on teacher TPACK (Technological Pedagogical Content Knowledge) and student writing outcomes (Zhang, 2024).”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Zhang, Y.
Affiliations
Beijing International Studies University (CN)
Corresponding author
Zhang, Yumei
Venue
International Journal for Lesson & Learning Studies
Publisher
Emerald
Published
2024-01-16
Volume
13
Issue
1
Pages
28-40
ISSN
2046-8253 (ISSN-L)
OpenAlex
W4390847716
Cited by
33
References
3
Citations by year
2026: 11 · 2025: 11 · 2024: 11
FWCI
8.71
Type
journal article
Language
en
Open access
closed
Locations
International Journal for Lesson and Learning Studies (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
  • Social Sciences › Education › Mathematics Education and Teaching Techniques
Keywords
lesson study, flipped classroom, EFL writing, TPACK, teacher professional development, learning outcomes, argumentative essay writing, college English teachers, technology integration in language teaching

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review