Learning Analytics and Impact on Personalized Student Learning
Bibliographic record
Abstract
This study explores the role of Learning Analytics (LA) in advancing personalized learning within educational institutions in Germany. Employing a qualitative approach, the research adopts a case study design to delve into the experiences of teachers, students, and education administrators in implementing LA. Data were collected through in-depth interviews and focus group discussions with participants who have direct engagement with LA in educational contexts. The findings demonstrate that Learning Analytics empowers educators to create highly personalized learning experiences, addressing the unique needs of individual students, while also providing learners with prompt and meaningful feedback. Nonetheless, the study identifies critical challenges, including inadequate teacher training in leveraging data effectively and persistent concerns surrounding the privacy and security of student data. Despite these hurdles, the integration of LA has proven to enhance the overall learning experience and supports the development of a more responsive and adaptive curriculum. To unlock the full potential of Learning Analytics, the study recommends the establishment of comprehensive policies focused on enhancing teacher training, integrating advanced technology, and ensuring transparency and ethical practices in data usage within educational settings.
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 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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.019 |
| 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".