Presentation Of The Paper "Improving Success/Completion Ratio In Large Surveys: A Proposal Based On Usability And Engagement" In Hcii 2017
Bibliographic record
Abstract
This is the presentation of the paper entitled “Improving success/completion ratio in large surveys: a proposal based on usability and engagement” in the Emerging interactive systems for education session at the HCI International 2017 Conference, held in Vancouver, Canada, 9 - 14 July 2017. This paper presents a research focused on improve the success/completion ratio in large surveys. In this case, the large survey is the questionnaire produced by the Spanish Observatory for University Employability and Employment. This questionnaire is composed by about 32 and 60 questions and between 86 and 181 variables to be measured. The research is based on the previous experience of a past questionnaire proposed also by the Observatory composed also by a large amount of questions and variables to be measured (63-92 questions and 176-279 variables). After analysing the target population of the questionnaire (also comparing with the tar-get population of the previous questionnaire) and reviewing the literature, the researchers have designed 11 proposals for changes related to the questionnaire that could improve the users’ completion and success ratios (changes that could improve the users’ trust in the questionnaire, the questionnaire usability and user experience or the users’ engagement to the questionnaire). These changes are planned to be applied in the questionnaire in two main different experiments based on A/B test methodologies that will allow researchers to measure the effect of the changes in different populations and in an incremental way. The proposed changes have been assessed by five experts through an evaluation questionnaire. In this questionnaire, researchers gathered the score of each expert regarding to the pertinence, relevance and clarity of each change proposed. Regarding the results of this evaluation questionnaire, the reviewers fully supported 8 out of the 11 changes proposals, so they could be introduced in the questionnaire with no variation. On the other hand, 3 of the proposed changes or improvements are not fully supported by the experts (they have not received a score in the top first quartile of the 1-7 Likert scale). These changes will not be discarded immediately, because despite they have not received a Q1 score, they received a score within the second quartile of that 1-7 Likert scale, so could be reviewed to be enhanced to fit the OEEU’s context.
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 | 0.102 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".