Abstract:

This research introduces a novel, automatic method to capture and analyze how students use iPads in educational settings. It overcomes the inaccuracies, limitations, or challenges tied to traditional data collection methods such as self-reporting, direct observations, and screen recordings (Parry et al. 2021, Baumgartner et al. 2023). Initiated in conjunction with the University of Würzburg's research project in the Aschaffenburg district, this study pioneers an approach for continuous and objective monitoring of iPad interactions among students.

To achieve this, the researchers developed and evaluated a new iPadOS application, leveraging Apple's “App Privacy Report” alongside the “Network Extension” framework. The App Privacy Report, an integrated Apple feature, records applications accessing data and sensors, as well as their network activities. Students participate by voluntarily sharing their App Privacy Report weekly through our custom application (Share Extension), designed to facilitate easy data transmission. Simultaneously, the Network Extension component autonomously monitors and logs an applications network traffic data, eliminating the need for manual student input.

These two innovative data sources - the App Privacy Report and Network Extension logs - serve as the foundational dataset for subsequent media usage analysis. They are transmitted to a backend system, where they undergo sophisticated processing to quantify the iPad usage during class. This involves the application of advanced analytical methods to decode the complex datasets, transforming raw datapoints into meaningful insights about student engagement with iPads. This backend processing is crucial for deriving actionable intelligence from the logged data, ensuring a comprehensive understanding of digital tool usage in educational environments.

Notwithstanding these challenges, the outcomes of this study underscore the viability and potential of the devised automatic methodology as a tool for exploring a 1:1 iPad integration within educational paradigms. This approach not only circumvents the biases and inaccuracies associated with self-reported data but also paves the way for more nuanced and large-scale analyses of digital tool usage in learning environments. Importantly, it offers significant contributions to educational research by enabling a deeper understanding of how iPads are used in classrooms.

Keywords:

Technology, iPad, assessment, automatic data collection, objective measures, log based measures.