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Student engagement within the development of learning analytics services in Higher Education is an important challenge to address. Despite calls for greater inclusion of stakeholders, there still remains only a small number of investigations into students' beliefs and expectations towards learning analytics services. Therefore, this paper presents a descriptive instrument to measure student expectations (ideal and predicted) of learning analytics services. The scales used in the instrument are grounded in a theoretical framework of expectations, specifically ideal and predicted expectations. Items were then generated on the basis of four identified themes (Ethical and Privacy Expectations, Agency Expectations, Intervention Expectations, and Meaningfulness Expectations), which emerged after a review of the learning analytics literature. The results of an exploratory factor analysis and the results from both an exploratory structural equation model and confirmatory factor analysis supported a two‐factor structure best accounted for the data pertaining to ideal and predicted expectations. Factor one refers to Ethical and Privacy Expectations, whilst factor two covers Service Feature Expectations. The 12‐item Student Expectations of Learning Analytics Questionnaire (SELAQ) provides researchers and practitioners with a means of measuring of students' expectations of learning analytics services. Lay Description: What is already known about this topic: Understanding student expectations of learning analytics is an important challenge for higher education institutions to address.Research has measured student beliefs regarding the features and ethical procedures of a learning analytics service. What this paper adds: This study builds on prior work by developing an instrument to measure student expectations of learning analytics services.This study proposes that student expectations of learning analytics can be measured using two subscales: (a) ethical and privacy expectations and (b) service expectations. Implications for practice and/or policy: Higher education institutions should understand what students expect from learning analytics services before any implementations are actioned.Higher education institutions have a validated instrument to gauge and understand student expectations of learning analytics services.Results obtained from the instrument can be used to inform the development of specific learning analytics policies for each higher education institution. [ABSTRACT FROM AUTHOR]

Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning practices, wherein online platforms are used as resources for both curricular learning activities and students' interest‐driven learning. Methods: The learning practices of 73 students were examined using a mixed‐methods design and a conceptualization of practices accounting for the context and purpose of their enactment. The dataset includes students' self‐reports on domain‐specific learning activities, three‐month‐long web‐browsing history of multiple platforms and stimulated‐recall interviews. The data were analysed through latent class and thematic analyses. Results and Conclusion: Five distinct patterns were found in the use of online platforms. These patterns show that different types of platforms were used purposely and in combination during curricular and interest‐driven activities aimed at learning software development. Moreover, students' purposes were driven by both the need to progress in their learning activities and the development of their interest in software development. The findings highlight the complexity of students' learning with online platforms, which develop quite extensively beyond curricular boundaries. Implications: The findings stress the need to recognize that undergraduate students' learning practices involve multiple online platforms and activities beyond the formal curriculum and that these play a key role in developing students' interests in learning software development. Moreover, our findings indicate the importance of taking into account the way students' learning practices unfold within platforms and how these relate to domain‐specific practices. Lay Description: What is already known about this topic?: Online platforms are mostly used by teaching staff in undergraduate education to support student learning during formal curricular tasks.Research knowledge is limited mostly to specific features of social networking platforms, such as Facebook Groups or instant messaging technologies.Students informally use interest‐driven activities and both social and domain‐specific platforms to support their learning.Research on students' interest in using massive open online courses suggested that students' learning is motivated by academic success, prospective economic opportunities, and personal interests. What this paper adds?: Students combine various types of online platforms purposely (e.g., search engines or Q&A sites), many of which are domain‐specific (e.g., Stack Overflow).Students use online platforms informally during work on curricular tasks to, for example, collaborate, solve problems, and understand concepts or methods.Domain‐specific (e.g., software engineering) platforms are used during other study‐related leisure and entrepreneurial activities.Online platforms play a key role in how students develop their interests in learning. Through them, students connect not only with other students but also with professionals, companies, and information about them. Implications of study findings for practitioners: Curriculum developers and faculty should consider preparing students to use disparate types of online platforms beyond what is included in the curriculum.Curriculum developers should also consider other activities beyond the classroom that are study‐related and meaningful for students and their learning. [ABSTRACT FROM AUTHOR]

Background: Problem‐solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE‐log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem‐solving activities are rarely investigated. Objectives: In this study, we examined how the theory‐informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior‐knowledge. Methods: A high‐frequency temporal data was collected with a camera, an electroencephalogram, and an eye‐tracker from 40 computer science students (bachelor and master studies) in the context of a code‐debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective. Results: Our findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code‐debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants. Implications: Extending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem‐solving skills. Lay Description: What is currently known about the subject matter: ● Problem‐solving interlinks cognitive, affective and metacognitive dimensions of learning. ● In digital settings instructors struggle to distinguish cognitive‐affective states of students. ● Learning technologies require interventions based on cognitive‐affective states of students. What the paper adds: ● When and what cognitive‐affective states are triggered during solving a programming problem. ● What and how measures as proxies for cognitive‐affective states are associated with performance. ● Emphasizes the benefits of augmenting IDE‐log data with sensor data in the context of learning design. Implications of study findings for practitioners: ● Methodology guidelines to identify and model variability in humans' natural behaviours. ● Importance of content and instructions personalization for student engagement and learning. ● Identification of multimodal data proxies for flexible and adaptive multimodal interfaces. [ABSTRACT FROM AUTHOR]

Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders' expectations of LA across Higher Education Institutions (HEIs) for large‐scale implementations that take their needs into account. Objectives: This study aims to contribute to knowledge about individual LA expectations of European higher education students. It may facilitate the strategy of stakeholder buy‐in, the transfer of LA insights across HEIs, and the development of international best practices and guidelines. Methods: To this end, the study employs a 'Student Expectations of Learning Analytics Questionnaire' (SELAQ) survey of 417 students at the Goethe University Frankfurt (Germany) Based on this data, Multiple Linear Regressions are applied to determine how these students position themselves compared to students from Madrid (Spain), Edinburgh (United Kingdom) and the Netherlands, where SELAQ had already been implemented at HEIs. Results and Conclusions: The results show that students' expectations at Goethe University Frankfurt are rather homogeneous regarding 'LA Ethics and Privacy' and 'LA Service Features'. Furthermore, we found that European students generally show a consistent pattern of expectations of LA with a high degree of similarity across the HEIs examined. European HEIs face challenges more similar than anticipated. The HEI experience with implementing LA can be more easily transferred to other HEIs, suggesting standardized LA rather than tailor‐made solutions designed from scratch. Lay Description: What is currently known about this topic: Implementing Learning Analytics is a promising but challenging task.SELAQ is a questionnaire that can be used to determine students' expectations of Learning Analytics.Several institutions in Europe applied SELAQ to implement Learning Analytics that meet the needs of their students. What this paper adds to this: This study applies SELAQ for the first time at a German university and compares the results to studies from the UK, Spain, and the Netherlands.The participating students at the German university show relatively homogeneous expectations of Learning Analytics.The expectations towards Learning Analytics of students across Europe are comparable. The implications of study findings for practitioners: Learning Analytics systems should be shared across institutions rather than being implemented separately as tailored solutions.More standardized or modular Learning Analytics systems are reasonable. [ABSTRACT FROM AUTHOR]

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations. Lay Description: What is already known about this topic: Many MLA systems are designed without learners' involvement.Understanding of how multimodal data can support learners' authentic needs is needed.MLA have the potential to enable the automated generation of models to provide real‐time feedback. What this paper adds: Understanding how learners perceive multimodal data and the learning context.The findings outline the soft challenges in using multimodal data in the learning practice.Ten dimensions were identified to serve as guidelines for researchers and designers.The paper reports learners' perspectives describing new, intriguing, and under‐developed ideas about potential uses of multimodal data in educational context.The discussion on the need for human‐centred design approaches for educational technologies has been presented. Implications for practice and/or policy: Relevant pointers for what is appropriate and ethically necessary when designing learning technologies.Change in perspective among educators, on how they address progress and engagement in learning. [ABSTRACT FROM AUTHOR]