Commentary: Clickers (personal response units) may add value to lectures
Why this work is in the frame
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Bibliographic record
Abstract
You can doubtless recall putting your hand up in a school class to eagerly answer a teacher's question. Some of that “let me answer” enthusiasm can be captured in large groups by the use of clickers, devices that look very much like a remote control for a television. They are familiar to many people as the means to get audience responses in television quiz programs. The personal response units digitally transmit a multiple choice alternative, perhaps even a word or phrase, to answer a question presented to a group. The encoded data can include the identity of the sending unit (and thereby the student in a class). The receiving software can provide rapid analysis of the class responses and the lecturer can respond appropriately to reward good choices, or correct errors of perception evident from the answering pattern. I have attended a number of demonstrations of these units and all sessions had technical glitches. Nevertheless I have colleagues who are enthusiastically using clickers and are spreading the word about how the use of personal response units enlivens and adds value to large-group lectures. I cannot embrace this technology with the enthusiasm of my adopting colleagues because it feels to me that school teaching approaches have been technologically dressed up and the pedagogical assumptions of clicker usage are inappropriate for tertiary students performing as adult learners. Use of this technology invites questions about the nature of the learning outcome, in that clickers used in simple scenarios may be reinforcing conditioned responses (training) rather than achieving higher intellectual outcomes. Most students and lecturers who use clickers like them [1]. Such analysis glosses over and ignores the lecturers and students who do not want to use this technology. I recall a conversation with one of my highest achieving medical students who asked me whether problem based learning (PBL) sessions were considered a success, and whether they would continue to be used in the local medical course (the subject might as well have been clickers as PBLs). The student rejected all the hyperbole about PBL teaching and considered it poor use of his time relative to focused lectures from experts. I extrapolate from this that the high achieving students would likewise not choose to be engaged in the “dead time” and conversations of error analysis that attend clicker sessions. Clickers have made an increasing impact on tertiary teaching as they have become more powerful, more convenient (radio transmitting), and more affordable. Also as a commercial commodity they are promoted at teaching conferences and are an attractive item to purchase with grant money for developing teaching. To many lecturers clickers must appear as a new technology, but it is merely a new variant of an old approach. As early as the 1960s whole lecture theaters were hard-wired to log student responses, but they never proved popular and I do not know of any sustained success stories that carried forward. A representative story is that of the experiences of John Cowan in civil engineering at Heriot-Watt university, Edinburgh, where he installed a “feedback classroom” in the early 1970s that was used mainly by John Cowan himself over approximately a five year period [2]. I am not alone in having doubts about the value of clickers, but there is also a valid positive case to be put so I commend a full reading of the evaluation of primary data presented by John Barnett of the University of Western Ontario [3]. The online version of Ref.3 requires a journal subscription so you might request a copy from the author directly at [email protected] Because statistics reveal little about why students like or dislike an approach it is helpful to look at individual comments. Firstly some cautionary comments from students at the University of Western Ontario, where some courses gave marks simply for attending and other courses graded clicker answers for credit. “I dislike how it took so long to get everybody up and running and how much class time was wasted explaining how the clicker works” [3]. “I really dislike clickers because I figure that I pay for my education and at this level I should not be graded for my participation. Some may call it easy marks, but I'm just fundamentally against it” [3]. Many students claimed that a good percentage of the questions posed in class were irrelevant or unhelpful and that cheating on clicker tests was rampant [3]. However there are good points to be made for the approach and the following comments come from students at the University of Glasgow. The use of clickers “encourages us to participate,” “it is more likely we will be forced to listen this way,” and “it lets the lecturer know what we do and don't understand” [1]. No single teaching approach suits all members of a class and integrated use of personal response units may be useful.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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