Digital Writing and Labor-based Grading: An Equitable and Inclusive Approach to Undergraduate Writing Instruction
Bibliographic record
Abstract
The tradition of digital writing instructional practices is nearly 50 years old in the US (Handa, 2004). Sometimes, students fully online digital writing courses may fail to engage with and finish the course because they do not feel competent in handling the technologies in the academic context; thus, it is important to find out what equitable practices and what factors influence student success in these courses. Therefore, this study aims to examine digital writing assignments requested in a writing course, with the goal of demonstrating an inclusive and equitable practice: the Labor-based grading contract, in a way that promotes equal and fair grades. This practice is proposed as a solution to the shortcomings detected, since it has been observed that students inexperienced in pre-college education in the delivery of online writing assignments persist in their difficulties with technological educational platforms in college. The creation of a contract between the teacher and the students -at first-, fosters knowledge, motivation, involvement, or engagement with the task; thus, digital writing assignments go from being an unattainable goal to being a feasible task to engage in. Also, the cooperative creation of this online writing with an easy-to-use platform (Eli Peer Review) stimulates them to persist in the following tasks, as they have already reflected on them and have already found out among their peers what they consist of and how to deal with such tasks. In our research we present a case study of a course based on online writing instruction. Therefore, this study aims to examine a particular course in the United States in which multimedia writing assignments and labor-based grading allowed for deep student engagement and success (Dickson,1974; Inoue, 2019). The data comes from the use of mixed methods that combine qualitative information collected through document analysis (teacher materials: syllabi, guidelines, instructions; student materials: personal research, blog entries, and final letter); classroom observation field diaries and the semi-structured teacher interview, with the quantitative methods of a student survey. The results show that there is a high degree of alignment between the course and the best practices of online instruction, and that the nature of the assignments and the Labor-based Grading Contract (Dickson,1974, Inoue, 2019) appear to play key roles in student engagement and success in the course. Likewise, the most highlighted aspect by the students has been the emotional factor, since the tasks have allowed them to get involved and enjoy writing in the digital support. The systematic observation of this writing course aims to deeply understand its provenance, objectives, taxonomy and functionality, with the final purpose of highlighting the capabilities of this methodology in order to offer it as a model in other contexts to promote a fairer and more equitable education.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".