Adapting Infosphère: Leveraging an OER Information Literacy Platform
Bibliographic record
Abstract
In the rapidly evolving landscape of higher education, ensuring that students possess robust information literacy skills is crucial. Infosphère – a comprehensive, modular online information literacy training platform originally developed and released under a CC BY license by the Université du Québec à Montréal – provides a dynamic, customizable framework to help learners refine their abilities in navigating complex information ecosystems. Leveraging its status as an open educational resource (OER), EPFL library training team adapted Infosphère’s content to better align with the specific curriculum needs of bachelor’s and master’s students at our institution, EPFL academic contexts, and language and technology specificities. This presentation will detail the process and rationale behind customization of Infosphère. We will discuss how the platform’s open license facilitated the adaptation and the revision of instructional materials to better address disciplinary nuances and student learning outcomes. By sharing these insights, we aim to highlight how the OER nature of Infosphère not only supported intellectual freedom and pedagogical innovation, but also enabled iterative refinements based on the exchanges with stakeholders. Attendees will gain practical knowledge about the implementation challenges and opportunities encountered throughout this adaptation journey. We will explore how our team collaborated with subject librarians, pedagogical and IT experts, and internal key players to establish a responsive, inclusive, and transparent development process. Moreover, we will discuss the adjustments needed to ensure that content and navigation patterns were accessible to learners with diverse backgrounds. This session underscores the transformative potential of openly licensed educational resources, showcasing how tools like Infosphère can be molded to meet local needs while retaining their core mission of cultivating critical information literacy competencies. Ultimately, by embracing the adaptability and scalability of OER-driven platforms, academic libraries can reaffirm their commitment to fostering equitable and meaningful learning experiences for students across all stages of their academic journeys.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".