Integration of Mobile AR Technology in Performance Assessment.
Bibliographic record
Abstract
Introduction Based on the popular educational philosophy of allowing students to develop diverse capabilities and achieve active knowledge building, performance assessment (PA) should be considered a vital link in teaching. In addition to assigning a final score to students, the purpose of assessment is to develop a highly in-depth understanding regarding the process that students undergo during learning and provide feedback to assist in student growth. Toptas (2011) indicated that an effective evaluation of the students who answered the questions in a particular period of time will be insufficient. If we want to correct this weakness, the performances of the students must be measured with the observation of the process as well. O'Neil and Osif (1993), and VanTassel- Baska (2014) indicated that assessment plays a vital role in teaching and that the process of assessment consists of goal setting, data collection, organisation, and result analysis. The results can be used to enhance teaching and report the actual progress of students. Turgut and Baykul (2012) point out that the process can be measured alongside the results of learning outputs by measuring the performances. In addition, it is asserted that the measurement of students' performance gives them the opportunity to effectively learn the concepts, complex events, and their structures. Nevertheless, a major problem encountered by the education community is determining the appropriateness of educational evaluation. PA has been recognised as one of the most effective methods for assessing this type of high-level thinking because this approach emphasises the application and demonstration of abilities in problem- solving situations and the complexity of problem-solving processes (Wiggins, 1993; VanTassel-Baska, 2014). Previous studies (Bay, Kucukoglu, Kaya, Gundogdu, Kose, Ozan, & Tasgin 2010; Jiang, Smith, & Nichols, 1997) have indicated that the primary limitations and disadvantages of the PA approach include the lack of comparison, limited reliability, unsatisfactory economic performance, and low validity. However, the majority of these factors can be attributed to the subjective consciousness of the assessors and errors in the measured situations. By contrast, augmented reality (AR) technology can be employed to display, in real situations, real- time information that is necessary for assessing or learning. From the perspective of cognitive psychology, this approach can be applied to reduce the errors resulting from the process of PA and to minimise the time and economic costs that teachers must bear when observing student behaviour. Therefore, we examined the meaning, relevant studies, and limitations of PA before investigating the effects that incorporating AR technology exert on improving PA systems. Subsequently, we applied an AR-based PA system to a cooking course to explore the effects of the application. The results of this study can serve as a reference for implementing PA in teaching. Performance Assessment (PA) Performance Assessment (PA) requires students to apply the knowledge and skills they have learned to perform hands-on practice rather than simply revalidating and recollecting the experience of learning (VanTassel-Baska, 2014). This assessment method satisfies the needs of the current trend of constructivist learning and teaching (Chang, 2002). PA motivates students to integrate the knowledge, skills, and dispositions required in the subject, and the results of the assessment can reflect students' problem-solving abilities in real life and the interest and needs of the students. Performance assessments, which can be conducted to evaluate high-level cognitive abilities and the dispositions and skills of students, are more comprehensive compared with conventional paper-based tests. When evaluating a student for periodic checks or a promotion, there has to be a list of measurable performance criteria that can be applied consistently to all members of a particular class. …
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".