Teaching, learning, and assessment activities used in additional language courses offered in blended contexts to promote the development of learners’ language skills in higher education
Bibliographic record
Abstract
This master’s dissertation explores the teaching, learning, and assessment activities that higher education instructors use in additional language courses in a blended format from an instructional design perspective. The types of activities that instructors use in order to develop learners’ language skills and the design of blended language courses have been under-researched in the literature, even though blended learning approaches in language learning have been used approximately since 2000 (Grgurović, 2017). Given the fast technology development, particularly web 2.0 technologies and other digital tools, and the possibility of learning an additional language, it becomes highly important to understand how additional language teaching and learning take place in these contexts. Moreover, the COVID¬ 19 pandemic has accelerated how technology is used by higher education institutions (Skulmowski & Rey, 2020). Therefore, in this study, the focus is on the language activities performed in the different types of blended courses, such as blended courses, blended online courses, and blended synchronous courses, as well as the mode in which they are performed: asynchronous, synchronous and face to face. The general objective of this study is to describe the additional language teaching, learning, and assessment activities used by instructors in blended courses to promote additional language skills development in higher education. To attain this objective, I formulated two specific objectives: 1. Describe instructors’ choices of teaching, learning, and assessment activities, and digital tools used in additional language blended courses. 2. Describe the complementarity between asynchronous, synchronous, and face to face activities’ modes. This study uses a qualitative methodology that is aligned with the objectives of the dissertation. The selected sample consists of three additional language instructors who taught English, French, and Spanish in a blended format at a university in Quebec province. The data collection methods include a semi-structured interview and the use of course documents. The data analysis methods consist of a descriptive thematic analysis and a documentary analysis. Moreover, the results and discussion are presented in the form of an article that has been submitted for publication to the Japan Association for Language Teaching Computer Assisted Language Learning Special Interest Group Journal (JALT CALL journal). It should be highlighted that this study uses secondary data from a larger study called “Élaboration et validation d'un modèle explicatif de la persévérance et de la performance dans les cours hybrides en enseignement supérieur” (Lakhal et al., 2019) which was subsidized by the Social Sciences and Humanities Research Council (SSHRC). The results of this study show that the activities performed in the blended courses for additional language learning are varied and are influenced by the affordances of technology and the blended courses’ modes. Generally, in these courses different skills and areas are developed: speaking, listening, reading, writing, subskills (i.e., grammar, vocabulary, and pronunciation), digital competency, metacognition, and content transfer and consolidation. The participants used the asynchronous mode for doing content transfer activities, developing learners’ digital competency, developing their comprehension skills, and preparing them for synchronous and/or face-to face modes. When it comes to synchronous and face to faced modes, the participants selected them to promote interaction and collaboration among learners, thus it involved the development of productive skills such as writing and speaking. Nevertheless, the four skills are spread in the different course modes, so they are not specific to only one mode. Concerning the digital tools used in the courses, they were diverse and mainly linked to the licenses available to the university. However, the study specifies a range of digital tools and the activities they support, some are specific to language teaching and learning. Finally, these results are relevant to the field in that they contribute to filling a gap in the scientific literature concerning the intersection between additional language teaching and learning and blended learning, they add knowledge to the discussion available in the scarce scientific literature about this intersection, and finally, they provide insights to language instructors in higher education teaching in blended formats about the different activities available to be taught in blended courses and reasons to implement them.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".