The Problems of Information Technologies Effectiveness in Humanities Education
Bibliographic record
Abstract
An impact of information technologies on high education in social and humanitarian sciences is considered. An analysis of modern teaching and assessment methods and means is given in a context of social challenges and signals of diminished interest in fundamental knowledge, over-formalization requirements for high education, application of existing tools of e-learning and assessment. It is pointed out that existing information and computer technologies and tools are not sufficient in terms of depth and qualities of assessment process, are weak in assisting of ideological understanding, do not help in formation of logic reasoning, understanding of cause-consequence dependencies, abilities of presenting results of one’s own thinking, assisting in improvement of argumentative skills and finding solutions. The limitations of electronics books use are also explained. Arguments are given in favour of importance of teacher’s roles such as mentor, leader, who indicates directions and corrects actions during knowledge delivery, the process of education and behaviour formation. Further improvement of methods of assessment using information and computer technologies is described as an interdisciplinary issue. The solution of this problem is possible only by consolidated efforts of scientists and lecturers from various knowledge domains, taking into account features and differences of disciplines and individual abilities of knowledge formation. A problem of efficiency improvement for information technologies application in humanitarian and social sciences is described. It’s shown that adaptation and redevelopment of system software to address cognitive and behavioural goals are required; in turn, modernization of learning and methodological education schemes should be based on the principles of inheritance, consistency, and social-cultural dependence.
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 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 | 0.047 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".