Usability of a classroom response system in an online course: Testing of a smartphone-downloadable technology enhanced learning tool for distance education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background and objective: Classroom response systems (CRS) have been used in higher education since the 1990s to enhance student learning and engagement. It began with portable “TV remote control-looking” devices that students used in class to answer questions posed by the professor. Aggregated responses are available instantaneously and projected on the screen to serve as a feedback mechanism for the professor and students to gauge learning, potentially prompt further review of the topics, or inspire further discussion. Companies which produce CRS tools are beginning to develop apps to allow students to use their own technology mobile devices during similar learning activities. Many educational institutions are increasingly offering distance education courses and programs, yet little is currently known about the effectiveness of CRS integration into online courses. This usability study was conducted to determine whether a technology enhanced learning tool, specifically a CRS that can be downloaded to one’s smartphone, would be suitable for adoption in online classes in one particular suburban university in New York.Methods: The study is a mixed method, one group, pretest/posttest descriptive design. Convenience sampling (n = 48) was used to engage students enrolled in an online nursing course during their first semester in a master’s degree program. A five-point Likert scale was designed for respondents to rate 21 statements in terms of their degree of agreement (with 5 being “strongly agree” and 1 being “strongly disagree”). The statements included descriptors of the three usability domains (functionality, support and effectiveness) selected to evaluate the smartphone-based CRS app. Open-ended questions were included to provide contextual perspectives on these criteria.Results: T-tests demonstrated an improvement in student ratings of agreement with the evaluative criteria for this CRS smartphone app when comparing pre- and post-implementation survey data. This includes agreement with the CRS’s functionality (p = .001), support (p = .004) and effectiveness (p = .189) at α = 0.05, as well as overall usability across criteria domains (p = .000 at α = 0.05). Respondents additionally suggested that specific features be changed or added to the current design to make it easier to navigate.Conclusions: For educational apps to achieve optimal use and effectiveness, iterative design assessments should continue until the end-users truly benefit from the technology enhanced learning tool. This smartphone-downloadable CRS app proved to be a useful adjunctive tool for enhancing student learning in an online class. Yet there were numerous design recommendations provided by students that could further improve its usability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it