Time-on-Task Metrics for Predicting Performance

Leinonen, Juho; Castro, Francisco Enrique Vicente; Hellas, Arto

doi:10.1145/3478431.3499359

Cited by 11 publications

(2 citation statements)

References 30 publications

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“…Predicting students' future success can allow instructors to proactively deploy interventions to help struggling students. Therefore, time-on-task is one key contributor to learning [31].…”

Section: Time On Taskmentioning

confidence: 99%

Proposed Model for the Excellent Educational System: Overview of the Field

Bantilan

2024

Asian J. Educ. Soc. Stud.

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The educational system must be transformed to fulfill its promise of providing quality education. In measuring its quality, it is vital to consider valid indicators to determine whether it has achieved its goals and objectives. Without indicators, it will be difficult to assess its performance and plan for improvement. Thus, this paper explored the various literature on best educational systems to list indicators and create a model of an excellent educational system. Based on the qualitative data analysis, four indicators emerged for the excellent educational system: context indicators, input indicators, process indicators, and output indicators. Context indicators include student characteristics, socio-economic conditions, cultural aspects, status of the teaching profession, and local community issues. Meanwhile, input indicators involve financial, material, and human resources that influence the success of an educational system. On the other hand, process indicators include how specific educational processes are conducted in practice like the application of standards, teaching quality, time on task, school climate, and educational leadership. Lastly, output indicators involve subject knowledge, competencies, repetition, progression and completion rates, and employer satisfaction. Further, these indicators are described as amiable context, adequate input, appropriate process, and acceptable output to achieve the best educational system. The absence of one indicator may affect other indicators as these indicators need one another to complete an educational system. Therefore, educational leaders are encouraged to focus on the improvement of these indicators to achieve an excellent educational system. Teachers and stakeholders can also support the administrators in realizing these indicators.

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“…Predicting students' future success can allow instructors to proactively deploy interventions to help struggling students. Therefore, time-on-task is one key contributor to learning [31].…”

Section: Time On Taskmentioning

confidence: 99%

Proposed Model for the Excellent Educational System: Overview of the Field

Bantilan

2024

Asian J. Educ. Soc. Stud.

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“…This could, for instance, help to further improve the calculation of the highly relevant time-on-task indicator. Time-on-task can be calculated differently depending on how sequences of inactivity are interpreted (Kovanović et al, 2015;Leinonen et al, 2022) but breaks in the interactions still present a challenge. A break in the activity stream could mean that a student was taking notes (i.e., more time spent engaged with the task) or that a student was using their smartphone (i.e., less time spent engaged with the task).…”

Section: Potential For Educational Technologies and Learning Analyticsmentioning

confidence: 99%

Technological responses to distracting media multitasking in academic learning

Biedermann

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With the rise of digitalization and ubiquity of media use, both opportunities and challenges emerge for academic learning. One prevalent challenge is media multitasking, which can become distracting and hinder learning success. This thesis investigates two facets of this issue: the enhancement of data tracking, and the exploration of digital interventions that support self-control. The first paper focuses on digital tracking of media use, as a comprehensive understanding of digital distractions requires careful data collection to avoid misinterpretations. The paper presents a tracking system where media use is linked to learning activities. An annotation dashboard enabled the enrichment of the log data with self-reports. The efficacy of this system was evaluated in a 14-day online course taken by 177 students, with results confirming the initial assumptions about media tracking. The second paper tackles the recognition of whether a text was thoroughly read, an issue brought on by the tendency of students to skip lengthy and demanding texts. A method utilizing scroll data and time series classification algorithms is presented and tested, showing promising results for early recognition and intervention. The third paper presents the results of a systematic literature review on the effectiveness of digital self-control tools in academic learning. The paper identifies gaps in existing research and outlines a roadmap for further research on self-control tools. The fourth paper shares findings from a survey of 273 students, exploring the practical use and perceived helpfulness of DSCTs. The study highlights the challenge of balancing between too restrictive and too lenient DSCTs, particularly for platforms offering both learning content and entertainment. The results also show a special role of media use that is highly habitual. The fifth paper of this work investigates facets of app-based habit building. In a study over 27 days, 106 school-aged children used the specially developed PROMPT-app. The children carried out one of three digital activities each day, each of which was supposed to promote a deeper or more superficial processing of plans. Significant differences regarding the processing of plans emerged between the three activities, and the results suggest that a child-friendly planning application needs to be personalized to be effective. Overall, this work offers a comprehensive insight into the complexity and potentials of dealing with distracting media usage and shows ways for future research and interventions in this fascinating and ever more important field.

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