Laptop initiative: Impact on instructional technology integration and student learning

The purpose of this study was to examine how 1:1 laptop initiative affected student learning at a selected rural Midwestern high school. A total of 105 high school students enrolled in 10th–12th grades during the 2008–2009 school year participated in the study. A survey instrument created by the Mitchell Institute was modified and used to collect data on student perceptions and faculty perceptions of the impact of 1:1 laptop computing on student learning and instructional integration of technology in education. Study findings suggest that integration of 1:1 laptop computing positively impacts student academic engagement and student learning. Therefore, there is need for teachers to implement appropriate computing practices to enhance student learning. Additionally, teachers need to collaborate with their students to learn and understand various instructional technology applications beyond basic Internet browsing and word processing.     

Beyond item analysis: Connecting student behaviour and performance using e-assessment logs

Traditional item analyses such as classical test theory (CTT) use exam-taker responses to assessment items to approximate their difficulty and discrimination. The increased adoption by educational institutions of electronic assessment platforms (EAPs) provides new avenues for assessment analytics by capturing detailed logs of an exam-taker's journey through their exam. This paper explores how logs created by EAPs can be employed alongside exam-taker responses and CTT to gain deeper insights into exam items. In particular, we propose an approach for deriving features from exam logs for approximating item difficulty and discrimination based on exam-taker behaviour during an exam. Items for which difficulty and discrimination differ significantly between CTT analysis and our approach are flagged through outlier detection for independent academic review. We demonstrate our approach by analysing de-identified exam logs and responses to assessment items of 463 medical students enrolled in a first-year biomedical sciences course. The analysis shows that the number of times an exam-taker visits an item before selecting a final response is a strong indicator of an item's difficulty and discrimination. Scrutiny by the course instructor of the seven items identified as outliers suggests our log-based analysis can provide insights beyond what is captured by traditional item analyses.

Practitioner notes

What is already known about this topic

• Traditional item analysis is based on exam-taker responses to the items using mathematical and statistical models from classical test theory (CTT). The difficulty and discrimination indices thus calculated can be used to determine the effectiveness of each item and consequently the reliability of the entire exam.

What this paper adds

• Data extracted from exam logs can be used to identify exam-taker behaviours which complement classical test theory in approximating the difficulty and discrimination of an item and identifying items that may require instructor review.

Implications for practice and/or policy

• Identifying the behaviours of successful exam-takers may allow us to develop effective exam-taking strategies and personal recommendations for students.
• Analysing exam logs may also provide an additional tool for identifying struggling students and items in need of revision.