Bibliographic record
Abstract
This issue of Higher Learning Research Communications (HLRC) features research and teaching proposals from both sides of the Atlantic. In a globalized and interconnected world, cooperation among researchers and higher education professionals is paramount. Equally important, as Steven Maranville argues in this issue’s featured essay, is recognizing being a scholar and researcher means being first and foremost a thinker. Higher education institutions are sanctuaries of knowledge, and faculty members are key in promoting understanding and the free flow of ideas among teachers and students.In the information Age, online tools have proven vital precisely in promoting knowledge and cooperation across oceans and frontiers. As more and more higher education institutions use the Internet in order to reach a wider student audience, the new challenges of online learning require new tools for faculty communication. As such, Eric Nordin and Peter John Anthony conducted research related to the development of a support website for online faculty. Such measures seem to be necessary, as they may lead to improvements in the quality of online teaching and learning.There is another research piece included in this issue that deals as well with improving the quality of teaching and learning. Luis Alberto D’Elia and Diane Wishart have investigated on both sides of the Atlantic, in Canada and Spain, how proper teacher training at the college level may lead to better youth engagement in science classes. Faculty in Education departments must become aware of the needs and challenges youth face in science learning in order to better train the teachers that will serve them. This is why more higher education professionals should engage in high school programs that prepare students for college.Oftentimes, previous educational experiences do not properly prepare students for college life, resulting in withdrawals, longer times to degree completion, or even unwillingness to complete a degree. Maxwell N. Kwenda investigated one of these aspects by trying to track and explain college credit completion in freshmen students. His results suggest high school academic performance, GPA, and college entrance exams can indeed predict academic success, which is why it is important to engage potential college students before they graduate from high school.This tenth issue of HLRC also features selected papers presented at the X Jornadas Internacionales de Innovación Universitaria [X International Conference on Innovation in Higher Education], celebrated by the Universidad Europea de Madrid, in Spain. The aim of the Jornadas is to promote research and ground-breaking teaching proposals in higher education. The selected papers reflect current pedagogic trends and incorporate innovative teaching strategies to engage college students and promote cross-sectional competences. Among the proposals, supporting research activities among students, promoting content and language integrated learning among faculty, providing practical experiences and cooperation in communication and audiovisual programs, student tutoring, taking into account the students emotional intelligence, and even using advanced computer software to provide International Relations students with the change to manage a transition to democracy from an authoritarian regime stand out.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.251 | 0.137 |
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".