Student engagement in technology rich classrooms and its relationship to professors' conceptions of effective teaching
Bibliographic record
Abstract
The benefit of computer related tools in supporting student learning is influenced by the engaging nature of the learning environment and the design of the learning activities. Professors have considerable role in the design of learning environments and activities and the way they design the environment is found to depend on their conceptions of teaching. However, professors' conceptions of (effective) teaching have not been studied in relation to technology use and student engagement. This dissertation study examined a) professors' conceptions of effective teaching and their perceived technology use in technology rich classrooms, and b) the nature and extent of student engagement in these classrooms and its relationship to professors' conceptions of effective teaching. Semi-structured interviews were used to obtain data from 13 professors who were teaching in active learning classrooms in a large research university in Eastern Canada in winter 2011. Interview questions focused on capturing professors' conceptions of effective teaching in relation to the course they were teaching in the classroom, their expected learning outcomes for students, their instructional strategies, and the role they saw for computers and the type of software they used and/or expected their students to use in relation to the course. Following interviews with the professors, a survey was administered to their students in the end of the term. The instrument, Student Engagement in Technology Rich Classrooms (SETRC) was developed to determine aspects and extent of student engagement in the context. Two hundred thirty two students consented to participate in the research and completed the paper copy of the survey. Analysis of interview data using a holistic inductive approach with constant comparison resulted in three conceptions of effective teaching—transmitting knowledge, engaging students, and developing learning independence. Transmitting knowledge highlighted organizing and presenting subject matter to students. Engaging students focused on student involvement in various activities such as discussion, presentation, collaboration, and hands on exercises. Developing learning independence and self-reliance related to holistic development of students as professionals and independent learners. This third conception also considered effective teaching to be designing learning environments with more emphasis on students' involvement. Principal component analysis with varimax rotation was applied to the student survey data. The analysis resulted in four components of student engagement: cognitive and applied engagement, social engagement, reflective engagement, and goal clarity. Subsequent multivariate analysis considering professors' conception as independent variable and the four student engagement components as dependent variables yielded significant relationship between professors' conceptions and student engagement. Students in classrooms of professors who consider effective teaching to be developing learning independence/self-reliance reported the highest score on cognitive and applied engagement; the score was the least for students in classrooms of professors who consider effective teaching to be transmitting knowledge. The difference was statistically significant. Concerning social engagement, students in classrooms of professors who consider effective teaching to be engaging students reported the highest score among the three groups and it was significantly higher than scores of students in classrooms of professors who consider effective teaching to be transmitting knowledge. Analysis results did not show any significant different in terms of reflective engagement and goal clarity. The study has implication for understanding conceptions of effective teaching in relation to computer use, determining students' course/classroom level engagement, and designing and assessing technology rich natural learning environments.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".