Transforming Teaching And Learning Using Tablet Pcs ? A Panel Discussion Using Tablet Pcs
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
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Transforming Teaching and Learning using Tablet PCs A Panel Discussion using Tablet PCs Abstract This panel discussion will highlight emerging best practices in the use of Tablet PCs to transform teaching and improve student success in college and university STEM (science, technology, engineering, math) courses. Faculty from two institutions, Colorado School of Mines and Rose- Hulman, will share their experience in using Tablet PCs and describe their approach to measuring the impact of their course redesign on student outcomes, sharing the evidence that supports their redesign efforts. Presenters will then lead a panel discussion with all the session’s presenters and the audience. The discussion will be facilitated by the use of Tablet PCs that are provided to the audience. The audience will use the Tablet PCs to access a free InkSurvey website to ask questions and respond to the panelists. This will model some of the ways that faculty have used Tablet PCs in their own classrooms to facilitate dialog and obtain instant, graphical feedback from students. Through this session, the audience will become participants and experience first-hand some of the innovations that are improving student achievement and engagement. This includes going beyond using classroom response “clickers” that are limited to multiple choice question types to using graphical feedback systems to ask open-ended questions that elicit underlying conceptual understanding and student misconceptions Colorado School of Mines At the Colorado School of Mines, Tablet PCs are used in junior-level engineering physics classes to promote active learning and facilitate real-time communication between instructors and students. We have developed InkSurvey, a web-based tool that allows instructors to pose open- ended questions to the students. Each student uses a Tablet PC to construct and submit a response, which can be text, free-formdrawings, graphs, equations, etc. The instructor can monitor these responses as they are submitted, providing an opportunity to offer solution hints and prepare a thoughtful response. Frank Kowalski will describe how this active learning experience promotes student metacognition and enables real-time feedback that can effectively guide the instructor in modifying or validating student understanding. InkSurvey can easily be used in conjunction with other computer-based or internet-based learning activities, such as applets. Significant learning gains, as evidenced by comparison of pre- and post-test scores, have been documented in classes at Colorado School of Mines.1
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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