The Effectiveness of Technology to Improve Educational Counseling Services: A Systematic Literature Review
Bibliographic record
Abstract
There is a scarcity of research that documents the use of technology-based educational counseling services specifically targeting students. This study’s aim is to compile and conduct a comprehensive review of the literature on the efficacy of technology in enhancing educational counseling services. Searches were conducted using the Publish or Perish (PoP) method throughout, along with Scopus, Crossref, PubMed, ACA, Web of Science, Springer, Emerald, as well as the Taylor and Francis databases. Data gathering was done in October and November 2023. The evaluation included a total of 19 papers, and the results indicated that technology has been proven to improve educational counseling services, where it is used in mental health that is dominated by MHAs, mobile well-being apps, mHEALTH, SMS, FER, and mindfulness apps. Computer-assisted and CD-ROM tools are used in personal counseling, while CAI is used in providing learning counseling. Social counseling used two technologies: a safety decision-aid smartphone app and a virtual message app. Counseling for learning was used with CAI, MCO, and video modeling. Career counseling employed a mobile-based career counseling app along with career counseling websites. The investigation included the countries of Indonesia, the United States, the United Kingdom, Türkiye (Turkey), the Philippines, and Iran.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".