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Supporting higher education students through analytics systems

Supporting higher education students through analytics systems Guest editorial Guest editorial Learning analytics and (big) data in higher education are emerging topics, however, empirical evidence as well as organisation-wide implementation are still scare (Ifenthaler, 2017). Research on student retention has been conducted predominantly in English-speaking countries such as Australia, UK or USA (e.g. Bean, 1982; Krause et al., 2005; Mah et al., 2019; Tinto, 1993). Findings highlight that students may benefit from analytics systems through personalised and adaptive support during their learning journey (Ifenthaler, Yau and Mah, 2019). For example, students often enter higher education academically unprepared and with unrealistic perceptions and expectations of academic competencies for their studies. Both, the inability to cope with academic requirements as well as unrealistic perceptions and expectations of university life, in particular with regard to academic competencies, are important factors for leaving the institution prior to degree completion (Mah and Ifenthaler, 2018). Analytics systems for supporting learning and teaching in higher education are slowly moving towards a mature field of research and development (Ifenthaler, Mah and Yau, 2019; Schumacher and Ifenthaler, 2018a, b; Schumacher et al., 2019). This broader (and system wide) adoption of analytics system provides new testbeds for empirical research and areas of new discoveries for the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Research in Higher Education Emerald Publishing

Supporting higher education students through analytics systems

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2050-7003
DOI
10.1108/JARHE-07-2019-0173
Publisher site
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Abstract

Guest editorial Guest editorial Learning analytics and (big) data in higher education are emerging topics, however, empirical evidence as well as organisation-wide implementation are still scare (Ifenthaler, 2017). Research on student retention has been conducted predominantly in English-speaking countries such as Australia, UK or USA (e.g. Bean, 1982; Krause et al., 2005; Mah et al., 2019; Tinto, 1993). Findings highlight that students may benefit from analytics systems through personalised and adaptive support during their learning journey (Ifenthaler, Yau and Mah, 2019). For example, students often enter higher education academically unprepared and with unrealistic perceptions and expectations of academic competencies for their studies. Both, the inability to cope with academic requirements as well as unrealistic perceptions and expectations of university life, in particular with regard to academic competencies, are important factors for leaving the institution prior to degree completion (Mah and Ifenthaler, 2018). Analytics systems for supporting learning and teaching in higher education are slowly moving towards a mature field of research and development (Ifenthaler, Mah and Yau, 2019; Schumacher and Ifenthaler, 2018a, b; Schumacher et al., 2019). This broader (and system wide) adoption of analytics system provides new testbeds for empirical research and areas of new discoveries for the

Journal

Journal of Applied Research in Higher Education – Emerald Publishing

Published: Jan 9, 2020

References

  • Student attrittion, intentions and confidence: interaction effects in a path model
  • Are higher education institutions prepared for learning analytics?