Patterns of reading behaviour in digital hypertext environments

Abstract

Background Computer‐based assessment allows for the monitoring of reader behaviour. The identification of patterns in this behaviour can provide insights that may be useful in informing educational interventions. Objectives Our study aims to explore what different patterns of reading activity exist, and investigates their interpretation and consistency across different task sets (units), countries, and languages. Three patterns were expected: on‐task, exploring and disengaged. Methods Using log data from the PISA 2012 digital reading assessment (9226 students from seven countries), we conducted hierarchical cluster analyses with typical process indicators of digital reading assessments. We identified different patterns and explored whether they remained consistent across different units. To validate the interpretation of the identified patterns, we examined their relationship to performance and student characteristics (gender, socio‐economic status, print reading skills). Results and Conclusions The results indicate a small number of transnational clusters, with unit‐specific differences. Cluster interpretation is supported by associations with student characteristics—for example, students with low print reading skills were more likely to show a disengaged pattern than proficient readers. Exploring behaviour tended to be exhibited only once across the three units: It occurred in the first unit for proficient readers and in later units for less skilled readers. Major Takeaways Behavioural patterns can be identified in digital reading tasks that may prove useful for educational monitoring and intervention. Although task situations are designed to evoke certain behaviours, the interpretation of observed behavioural patterns requires validation based on task requirements, assessment context and relationships to other available information.

Understanding students’ problem-solving patterns: Evidence from an allotted response time in a PISA 2012 item

“…Conversely, if the response time is analyzed at the item-level, by subdividing the total response time into specific steps, we can identify students’ cognitive processes and strategies to address the item (e.g., Hahnel et al, 2022 ). In particular, if an item consists of several decision-making steps, like those items in the PISA 2012 CPS task, the time-related variables for each step of addressing the item can be identified.…”

“…In short, studies related to response time have analyzed response speed or the time allotted to the either test-level (e.g., Engelhardt and Goldhammer, 2019 ) or item-level (e.g., Ren et al, 2019 ; Hahnel et al, 2022 ). Still, with test-level analysis, it is difficult to identify the precise step in which students spend a lot of time on an item.…”

Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?

“…The two contributions of He et al (2023) and Hahnel et al (2023) demonstrate a surprisingly similar approach to identifying behavioural patterns in digital reading. Both applied methods of cluster analysis using a large international student sample and associated the identified cluster with students' reading performance and sociodemographic characteristics.…”

“…The paper by He et al (2023) is remarkable in that the authors used the method of dynamic time warping to identify clusters based on navigation and time sequences. The paper of Hahnel et al (2023), in comparison, used hierarchical clustering based on process indicators from nine digital reading tasks and investigated cluster differences across tasks, languages, and countries.…”

Reading English Texts through Telegram App: Indonesian Vocational Students’ Perceptions

“…Engaging the students in reading texts is necessary for acquiring input and producing meaningful output (Indrayadi, 2021;Safaie, 2020;Soto et al, 2019). As one of the most crucial information processing skills (Hahnel et al, 2022), which depends on cognitive and linguistic processes (Nation, 2019), reading can influence students' academic success (Mulatu & Regassa, 2022;Alisaari et al, 2018). Generally, the utility of technology for reading creates critical challenges for education in terms of the speed of processing, text recall, and reading comprehension (Anggraini, Cahyono, et al 2022;Beard, 2021).…”