The impact of attendance on first-year study success in problem-based learning

Bijsmans, Patrick; Schakel, Arjan H.

doi:10.1007/s10734-018-0243-4

Cited by 50 publications

(34 citation statements)

References 37 publications

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“…Even though many academic support programmes have been implemented (Padgett et al 2013), and research on study success is extensive (Attewell et al 2006;Bijsmans and Schakel 2018;Morosanu et al 2010;Schmied and Hänze 2015), dropout rates in higher education remain at about 30% in the Organization for Economic Cooperation and Development member countries (OECD 2019). Student dropout has consequences on different levels, such as for the individual, the higher education institution, and for society (Larsen et al 2013).…”

Section: Study Successmentioning

confidence: 99%

“…Moreover, there are several theoretical perspectives and models of student success in higher education (Bean and Metzner 1985;Rovai 2003;Tinto 1982), and many share common factors, even though their emphasis varies. Such common factors, which are related to study success include students' sociodemographic factors (e.g., gender, ethnicity, family background), cognitive capacity, or prior academic performance (e.g., grade point average [GPA]), and individual attributes (e.g., personal traits, and motivational or psychosocial contextual influences) as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness (Bijsmans and Schakel 2018;Brahm et al 2017;Remedios et al 2008;Tinto 2017). To sum up, the essence of study success is to capture any positive learning satisfaction, academic improvement, or social experience in higher education.…”

Section: Study Successmentioning

confidence: 99%

“…Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent (Sarrico 2018). Factors affecting study success range from individual dispositions and characteristics such as age, gender, motivation or prior academic performance to features of the educational environment, such as curriculum design, learning tasks or social components (Bijsmans and Schakel 2018;Tinto 2005). The essence of study success is to capture any positive learning satisfaction, improvement, or experience during learning.…”

Section: Introductionmentioning

confidence: 99%

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Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

220

109

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Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

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Section: Study Successmentioning

confidence: 99%

Section: Study Successmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

220

109

View full text Add to dashboard Cite

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“…In addition, the students' grades are determined based on their ability to provide quality output within defined, pre-determined time limits (Heiman and Kariv, 2004). Another important factor is a student's ability to attend class regularly and on time, which was found by Bijsmans and Schakel (2018) to be a direct determinant of academic performance and has a substantial impact on end-of-year grades.…”

Section: Student Role and Time Organizationmentioning

confidence: 99%

Interaction between time organization and participation dimensions among higher education students

Porath

Rosenblum

2018

British Journal of Occupational Therapy

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Introduction: In today's academic world, higher education students from all study areas are expected to simultaneously cope with a variety of assignments, which increases stress levels. This tension may affect productivity, health and quality of life. Hence, it is important for students to adopt effective time organization and management techniques. This study aims to characterize time organization and participation dimensions, and the relationships between those dimensions among undergraduate students. Method: The research sample consisted of 60 undergraduate students who completed several questionnaires: demographic, time organization and participation scales assessing daily time organization; an occupational questionnaire; and additional questions for assessing further daily participation dimensions that are unique to students. Results: Time-organization ability related to and maintained a predictive relationship with individual daily participation aspects. Particularly, academic and daily routine constituted a majority (44%) of the students' time. Regression analysis indicated that the way the students' daily activities were organized in terms of time use explained between 12% and 18% of the variance of daily participation dimensions as performance, enjoyment and motivation levels. Conclusion: Time-organization abilities significantly affected students' participation dimensions. Further research is recommended to help develop an assessment and intervention programme suiting student needs and enhancing their productivity potential and wellbeing.

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mentioning

confidence: 99%

Moving to an Attendance Control System. Does It Matter?

Asanov¹,

Lopes²,

Faizova³

2019

EDULEARN Proceedings

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The scientific community not always agree when discussing the benefits or problems with using student attendance systems. Some refer that these systems are expensive, flawed and demotivate students [1], while others argue that one of the main factors for academic failure is not being in class [2]. Regardless of this discussion, there is the need for leadership to optimize higher education resources, such as staff, classrooms, laboratories and teachers. With the democratization of higher education and the opening of new opportunities, more students are pursuing education at a high level, leading to increasing number of students, with diverse academic backgrounds, economic status and culture. Universities have to adapt, within budget constraints and in the assumption of the commitment to their social and economic development mission.In this context, it is necessary to separate "mandatory attendance", in which a student is required, by regulations, to attend to a specific percentage of classes, from "attendance control", which allows to get valuable information for management and allocation of resources. The choice of enforcing a minimum number of presences in class depends on the policy of the scientific and pedagogical bodies. However, the information about student attendance represents valuable management information that can be used for informed decision making regarding the university operation.Regardless of this discussion, some universities, such as the Taraz State University, are currently performing manual registration of attendance. In each class, the teachers register the students' attendance, and, after class, they transcribe this information to a digital platform, to be consulted by the faculties' management body. This is a repetitive, boring and error prone process, taking valuable time from teachers and harming other scientific or pedagogical activities.In this paper, we describe the design, development and implementation of a digital attendance control management system, adequately integrated with other university's services, such as class timetables, b-learning platform, registration record office, and others. The system is based on open source programming languages, protocols and tools, and composed of hardware devices to be installed in the classroom and servers to receive and save the information in a database. Each student can use both an RFID card and smartphone to register attendance, that will be immediately accessible to the management bodies. Teachers can validate the students' attendance in each class, ensuring that the information is correct and up to date.

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The impact of attendance on first-year study success in problem-based learning

Cited by 50 publications

References 37 publications

Utilising learning analytics to support study success in higher education: a systematic review

Utilising learning analytics to support study success in higher education: a systematic review

Interaction between time organization and participation dimensions among higher education students

Moving to an Attendance Control System. Does It Matter?

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