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Goal setting is vital in learning sciences, but the scientific evaluation of optimal learning goals is underexplored. This study proposes a novel methodological approach to determine optimal learning goals. The data in this study comes from a gamified learning app implemented in an undergraduate accounting course at a large German university. With a combination of decision trees and regression analyses, the goals connected to the badges implemented in the app are evaluated. The results show that the initial badge set already motivated learning strategies that led to better grades on the exam. However, the results indicate that the levels of the goals could be improved, and additional badges could be implemented. In addition to new goal levels, new goal types are also discussed. The findings show that learning goals initially determined by the instructors need to be evaluated to offer an optimal motivational effect. The new methodological approach used in this study can be easily transferred to other learning data sets to provide further insights.
Highlights
• Students have limited concerns about privacy in learning analytics (LA).
• Students' privacy concerns in LA vary across countries.
• Culture shapes students' privacy concerns in LA.
• Power distance, uncertainty avoidance and masculinity affect privacy concerns in LA.
• Cultural values should be considered in LA privacy management.
Abstract
Applications of learning analytics (LA) can raise concerns from students about their privacy in higher education contexts. Developing effective privacy-enhancing practices requires a systematic understanding of students’ privacy concerns and how they vary across national and cultural dimensions. We conducted a survey study with established instruments to measure privacy concerns and cultural values for university students in five countries (Germany, South Korea, Spain, Sweden, and the United States; N = 762). The results show that students generally trusted institutions with their data and disclosed information as they perceived the risks to be manageable even though they felt somewhat limited in their ability to control their privacy. Across the five countries, German and Swedish students stood out as the most trusting and least concerned, especially compared to US students who reported greater perceived risk and less control. Students in South Korea and Spain responded similarly on all five privacy dimensions (perceived privacy risk, perceived privacy control, privacy concerns, trusting beliefs, and non-self-disclosure behavior), despite their significant cultural differences. Culture measured at the individual level affected the antecedents and outcomes of privacy concerns. Perceived privacy risk and privacy control increase with power distance. Trusting beliefs increase with a desire for uncertainty avoidance and lower masculinity. Non-self-disclosure behaviors rise with power distance and masculinity and decrease with more uncertainty avoidance. Thus, cultural values related to trust in institutions, social equality and risk-taking should be considered when developing privacy-enhancing practices and policies in higher education.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.
An exploratory latent class analysis of student expectations towards learning analytics services
(2021)
For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class analysis to understand whether student expectations of learning analytics services can clearly be segmented, using self-report data obtained from a sample of students at an Open University in the Netherlands. The findings show that student expectations regarding ethical and privacy elements of a learning analytics service are consistent across all groups; however, those expectations of service features are quite variable. These results are discussed in relation to previous work on student stakeholder perspectives, policy development, and the European General Data Protection Regulation (GDPR).