High‐tech versus high‐touch education: perceptions of risk in distance learning
Bibliographic record
Abstract
Purpose As colleges implement alternative forms of education delivery, prospective students must consider the method of instruction when choosing a post‐secondary institution. The purpose of this research paper is to assess the search criteria considered most important to prospective undergraduate students and to evaluate their preference for online versus on‐campus instruction. Design/methodology/approach This paper reviews a selection of literature on college choice behavior, with special reference to on‐campus (high‐tech) versus online (high‐touch) delivery. A pilot study together with a conjoint methodology is used to measure the importance students place on method of instruction, relative to several traditional criteria. Findings The conjoint results identify two unique student segments (risk‐sensitive and cost‐sensitive) based on attitudes toward high‐tech versus high‐touch delivery. While the risk sensitive segment expresses strong preference for high‐touch delivery, the cost sensitive segment is open to high‐tech delivery, if the price is right. Practical implications Many studies have concluded that online education may be more suited for mature, graduate students. This study, however, identifies an undergraduate student segment with a propensity toward high‐tech education. As online technology continues to diffuse through society, prospective undergraduates are expected to become less averse to alternate means of instruction. Originality/value Although many studies have compared online versus on‐campus learning, few, if any, have examined the attitudes of prospective students applying to a post‐secondary program, having no experience with distance education. This study focuses on the needs of prospective undergraduate students, highlighting differences in attitude toward cost and risk.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".