The Development of Students' Assessment Literacies as They Transition to University: An Exploratory Case Study
Bibliographic record
Abstract
Changes in academic demands, expectations, and ways of demonstrating knowledge through assessments are among the challenges faced by students transitioning to university. There is transition research in the Canadian context, but little documenting students' experiences with assessment, or how they develop their assessment literacies (e.g., understanding assessment in the course context and how assessment information is used to monitor and improve learning). This research examined how first-year university students' experiences with, knowledge of, and expectations about assessment impacted the development of their assessment literacies as they transitioned to university. The exploratory case study was theoretically framed by social cognitive theory (Bandura, 1977a, 1986) and reflexivity (e.g., Ryan, 2015; Schön, 1983, 1987). Three data sources were collected from ten first-year student participants: course assessment documents that triangulated students' responses from two semi-structured interviews and students' assessment journal entries. These data were coded using Saldaña's (2016) structural, emotion, and values coding. Course assessment documents were compared and categorized (Maxwell & Miller, 2008), noting the social setting and social actors (stakeholders) involved (Coffey, 2014). The findings illuminated four primary impacts on students' development of assessment literacies: 1) multiple interacting literacies needed to facilitate success; 2) social (i.e., personal and academic) supports; 3) variability in teaching staff; and 4) assumptions made by institutional stakeholders about what students know and can do. These impacts resulted in navigational work (extra work beyond typical class and assessment preparation), but also the development and implementation of navigational strategies used to cope with the new academic expectations and demands of university. The findings led to a working model that characterizes how students learn and are empowered by collaborating with teachers and acting reflexively and autonomously within a cycle of teaching, assessment, and learning that is facilitated by formative feedback. This dissertation research adds to the literature on the development of students' assessment literacies, and highlights the navigational strategies that facilitated student participants' development of assessment and other literacies as they met the academic demands of university. Increased understanding of such development and what promotes it may facilitate greater student success in the first year of university and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".