Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts' opinions on the system architecture were at the most appropriate level.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| 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 it