A Case Study and Call to Action: Incorporating the ACRL Framework for Information Literacy in Undergraduate CS Courses
Bibliographic record
Abstract
Information literacy (IL) is fundamentally important for CS students and graduates who are required to write research papers and stay abreast of new technologies and ideas. However, IL is absent in CS curriculum guidelines and the literature is scarce on research focused on IL skills among CS students. In this paper, we discuss aspects of IL and introduce the ACRL Framework for Information Literacy in Higher Education in the context of an undergraduate CS course covering social issues. We share how we used the Framework as the basis of our learning activities, which included lectures, a reading, and an assignment in which students reflected on core ideas pertaining to IL. We analyzed responses from the assignment to assess whether students achieved our learning outcomes. Nearly all students recognized markers of scholarly authority, but fewer students achieved learning outcomes based on more abstract concepts. We provide recommendations on incorporating IL activities in CS courses, and encourage explicit interventions to improve CS students' IL skills.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".