Holistic Language Learning: Implementing Authentic Assessment to Cultivate 4C Skills in Chinese University English Course
Bibliographic record
Abstract
This study investigates the effect of authentic assessment in cultivating the 4Cs (Communication, Collaboration, Critical Thinking, and Creativity) among undergraduate students enrolled in the English language course. The escalating demand for graduates with a holistic skill set and the decay of conventional testing necessitates innovative assessment methods differentiated from conventional testing. The 4C skills play an imperative role in preparing students for the complexities of the contemporary workforce and demanding studying and working contexts. Therefore, the study sets the objective to examine the effect of authentic assessment that fosters the integration of 4C skills into the curriculum. The study employs a quasi-experimental design incorporating a quantitative data collection method to evaluate the effectiveness of authentic assessment. Authentic assessment is taken as an intervention in the experiment. The outcomes of the intervention demonstrated significant improvements in students' Communication, Critical Thinking, and Creativity, with no significant effect on Collaboration. The paper explores the implications of these findings. Recommendations are provided for educators to implement similar frameworks tailored to their specific contexts. The significance of the study lies in its potential to reshape assessment practices and produce graduates better equipped to meet the evolving demands of the professional landscape. The authentic assessment study offers a promising avenue for educators to cultivate the 4C skills essential for success in the modern world. This research contributes to the ongoing discourse on innovative assessment methodologies and their impact on undergraduate education.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".