Does Integrating Technology-Based Attendance into Teacher Education Program Improve Student Achievement in Kuwait?
Bibliographic record
Abstract
This research addresses the benefits of integrating a technology-based attendance system based on classroom management techniques (CAMTs and CARs) in the teacher education program in Kuwait. Several areas of attendance research are described, such as the importance of using technology in developing systems-based research, and the development of a technology-based attendance-based program involving CAMTs and CARs, which includes basing the design of the technology-based attendance on three colors, stating procedural applications, and saving and closing procedures. Results indicated that use significantly improved student attendance and achievement in teacher education. Finally, several benefits of using an attendance system are addressed and recommendations are offered for instructors and administrators in teacher education, and for future research. Introduction Internationally, renewed interest in higher education has led most universities to implement the most up-to-date technologies. In recent years, a vast number of universities also have begun to make technological advances and systems implemented within educational services and resources available to students and teaching staff in educational programs, and to the community as well (Aksal, 2009; Ellis, 2006; Kuzu, 2009; McGill & Klobas, 2009; West, Waddoups, & Graham, 2007). Technology is an important tool, providing users with professional solutions and applications necessary to work on everyday educational issues (Firth, Lawrence, & Looney, 2008; Friedman, 2007; Kuzu, 2009). Technology is defined as how people modify the natural world to suit their own purposes--that is, everything people use to extend human abilities and satisfy human needs and wants in a certain manner (Henniger, 2004, p. 163). Technology can be designed and used in learning objectives, built from a collection of static content that helps users add and retrieve needed information according to any model of user-centric systems (Schatz, 2005). Technology use in university classrooms can have a great impact on higher education (Fitch, 2004). Johari and Bradshaw's (2008) study noted the importance of technology as a powerful motivator in enhancing learning through the use of several motivational techniques based on theories of leaning. Technology integration into university classes takes several forms and offers several benefits. Technological advances provide useful ways to facilitate and enhance teaching and learning in educational settings (Friedman, 2007; Ryba, Sleby, & Nolan, 1995; Sadik, 2008). In addition, technological advances and tools (i.e., computers, digital and datashow projectors, PeopleSoft, technological systems, Blackboard, and WebCT) have been implemented in higher education settings and used by administrators and instructors in teaching, learning, and monitoring student performance and progress. Furthermore, technology provides students opportunities to practice and experience related activities that support their learning (Sefton-Green, 2006). Firth, Lawrence, and Looney (2008) showed, for example, that students' interest in class attendance was enhanced through the use of technology in lectures and the offering of other classes on learning topics that involved technology practices. Other research (Finlay, Desmet, & Evans, 2004; Prensky, 2009; Shurville, Browne, & Whitaker, 2009) has emphasized the importance of incorporating current technological tools in the development of any modern educational system. In research by McGill and Kobas (2009) and Bulger, Mayer, Almeroth, and Blau (2008), the focus has been on the use of research-based results and technology to ensure significant and positive outcomes for student performance. Thus, findings from technology-based research can be used to assist administrators, professors, teaching staff, and students in higher education institutions to implement these developed research strategies effectively in teaching and learning, and thereby affect students' behaviors in classrooms. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".