The impact of affect-aware support on learning tasks that differ in their cognitive demands

This paper investigates the effect of affect-aware support on learning tasks that differ in their cognitive demands. We conducted a study with the iTalk2learn platform where students are undertaking fractions tasks of varying difficulty and assigned in one of two groups; one group used the iTalk2learn platform that included the affect-aware support, whereas in the other group the affect-aware support was switched off and support was provided based on students’ performance only. We investigated the hypothesis that affect-aware support has a more pronounced effect when the cognitive demands of the tasks are higher. The results suggest that students that undertook the more challenging tasks were significantly more in-flow and less confused in the group where affect-aware support was provided than students who were supported based on their performance only

Light Bulb Moments in the Classroom: Probing Design Opportunities for Ambient LA Displays in Higher Education

[EN] Teachers in higher education are tasked with the demanding job of providing support tailored to each individual student’s need. To provide tailored support, teachers need to accurately monitor students’ activities and decide on appropriate support interventions. Learning analytics applications have the potential to aid teachers to maintain an overview of their students’ activities. However, those applications are often designed as centralized graphical displays, taking teachers’ attention away from the classroom and sometimes overburdening teachers. Therefore, we investigate whether ambient LA displays offer a solution to complement traditional LA applications, as these systems are designed as objects that integrate seamlessly into the classroom context. We conducted an exploratory study in Higher Education to investigate teachers’ needs for information and their perception of ambient LA displays in relation to their teaching practice. We formulate three key findings and a set of design opportunities that flow from these findings to inform future work of supporting the HE context with ambient LA displays.We thank P.H.A. Teuwen, Y.J. Thijssen, and N.T. van de Ven for helping in the research and preparing the visual materials. We thank all the involved teachers and students. Our thanks also go to Dr. Bakker, Prof. Eggen, and Dr. Chuang who have supported this work.An, P.; Van Leeuwen, A. (2021). Light Bulb Moments in the Classroom: Probing Design Opportunities for Ambient LA Displays in Higher Education. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 215-223. https://doi.org/10.4995/HEAd21.2021.12807OCS21522

Teachers’ experiences of monitoring their students in online higher education: recommendations for course design and opportunities for learning analytics

Owing to the worldwide pandemic, use of technology and online education has increased. Studies into teachers' experiences in Higher Education indicated that teachers find it hard to monitor their students' progress during online education. Adequate teacher monitoring is essential, since it allows teachers to adapt their teaching strategies to student needs. Therefore, it was investigated what monitoring strategies teachers employed in online education and what challenges they experienced. Interviews were held with 10 teachers. The results showed that teachers primarily monitored students during online guided sessions. A difficulty was the lack of non-verbal and other observational cues that are normally available. To deal with this challenge, teachers created explicit monitoring opportunities by adjusting their course design. In the discussion, recommendations are given to stimulate teacher monitoring, and the potential role of automated analyses (learning analytics) to aid teacher monitoring is discussed

Participatory design of teacher dashboards: navigating the tension between teacher input and theories on teacher professional vision

In the field of AI in education, there is a movement toward human-centered design in which the primary stakeholders are collaborators in establishing the design and functionality of the AI system (participatory design). Several authors have noted that there is a potential tension in participatory design between involving stakeholders and, thus, increasing uptake of the system on the one hand, and the use of educational theory on the other hand. The goal of the present perspective article is to unpack this tension in more detail, focusing on the example of teacher dashboards. Our contribution to theory is to show that insights from the research field of teacher professional vision can help explain why stakeholder involvement may lead to tension. In particular, we discuss that the sources of information that teachers use in their professional vision, and which data sources could be included on dashboards, might differ with respect to whether they actually relate to student learning or not. Using this difference as a starting point for participatory design could help navigate the aforementioned tension. Subsequently, we describe several implications for practice and research that could help move the field of human centered design further

Learning analytics and societal challenges: Capturing value for education and learning

ABSTRACT A complex challenge for the society is to offer equal learning opportunities at various life stages and to support students, teachers, and institutions in their various tasks and roles related to learning and teaching. Learning analytics (LA) provides an opportunity to address these societal challenges. As the LA field matures, tool development is aimed at aiding informed human decision-making and combating inequalities. For example, detecting students at risk of dropping out or supporting self-regulated learning. The inception of LA was catalysed by an increasing amount of available data and what could be done with these data to improve learner support and teaching. Simultaneously, an increase in the computational power, machine learning methods, and tools at hand offer renewing affordances to analyse and visualise data both retrospectively and for predictive purposes. Employing LA as a solution also brings potential problems, such as unequal treatment, privacy concerns, and unethical practices. Through selected example cases, this chapter presents and addresses these potentials and risks.ABSTRACT A complex challenge for the society is to offer equal learning opportunities at various life stages and to support students, teachers, and institutions in their various tasks and roles related to learning and teaching. Learning analytics (LA) provides an opportunity to address these societal challenges. As the LA field matures, tool development is aimed at aiding informed human decision-making and combating inequalities. For example, detecting students at risk of dropping out or supporting self-regulated learning. The inception of LA was catalysed by an increasing amount of available data and what could be done with these data to improve learner support and teaching. Simultaneously, an increase in the computational power, machine learning methods, and tools at hand offer renewing affordances to analyse and visualise data both retrospectively and for predictive purposes. Employing LA as a solution also brings potential problems, such as unequal treatment, privacy concerns, and unethical practices. Through selected example cases, this chapter presents and addresses these potentials and risks

The function of teacher dashboards depends on the amount of time pressure in the classroom situation: Results from teacher interviews and an experimental study

Teacher dashboards are visual displays that provide information to teachers about their learners. In this article, we address teacher dashboards in the context of computer-supported student collaboration in primary education. We examine the role of different types of dashboards for the specific purpose of aiding teachers in identifying which group of collaborating students is in need of support. This question is addressed using qualitative and quantitative approaches. First, an interview study is reported in which teachers’ views (n = 10) on and perceptions of the acceptability of different types of dashboards were examined. Then, the results of an experimental vignette study are reported, which built upon on the interview study, and in which teachers (n = 35) interacted with mirroring or advising dashboards. Together, the studies revealed that the classroom situation, such as differing levels of time pressure, plays an important role regarding what type of dashboard is beneficial for a teacher to use in the classroom. The theoretical contribution of our study lies in a conceptual and empirical investigation of the relation between teachers’ need for control and their perception of different types of dashboards. Our study also points to several practical implications and directions for future research

A mixed method approach to studying self-regulated learning in MOOCs: combining trace data with interviews

To be successful in online education, learners should be able to self-regulate their learning due to the autonomy offered to them. Accurate measurement of learners’ self-regulated learning (SRL) in online education is necessary to determine which learners are in need of support and how to best offer support. Trace data is gathered automatically and unobtrusively during online education, and is therefore considered a valuable source to measure learners’ SRL. However, measuring SRL with trace data is challenging for two main reasons. First, without information on the how and why of learner behaviour it is difficult to interpret trace data correctly. Second, SRL activities outside of the online learning environment are not captured in trace data. To address these two challenges, we propose a mixed method approach with a sequential design. Such an approach is novel for the measurement of SRL. We present a pilot study in which we combined trace data with interview data to analyse learners’ SRL in online courses. In the interview, cued retrospective reporting was conducted by presenting learners with visualizations of their trace data. In the second part of the interview, learners’ activities outside of the online course environment were discussed. The results show that the mixed-method approach is indeed a promising approach to address the two described challenges. Suggestions for future research are provided, and include methodological considerations such as how to best visualize trace data for cued retrospective recall. &nbsp

The role of reference frames in learners’ internal feedback generation with a learning analytics dashboard

Being able to self-regulate can positively impact learners’ academic achievement. An inherent catalyst of Self-Regulated Learning (SRL) is internal feedback, the new knowledge which is generated when comparing current knowledge against reference information. Learners may not always generate internal feedback, hampering further SRL. Supporting SRL can be done with a Learning Analytics Dashboard (LAD), in which reference frames allow for comparisons and facilitate internal feedback generation. This study explores internal feedback generation using a LAD and the effect of reference frame availability. A multiple method design examined the interplay of reference frames, comparison processes, internal feedback generation and preparatory activities engagement. Differences between three conditions were explored using Bain ANOVA's. Results showed that reference frames almost exclude other external comparators and are used in parallel with an internal comparator. A peer reference frame leads to most verbalizations of internal feedback, and potentially to most verbalizations of preparatory activities

Learning Analytics and societal challenges: Capturing value for education and learning

A complex challenge for the society is to offer equal learning opportunities at various life stages and to support students, teachers, and institutions in their various tasks and roles related to learning and teaching. Learning analytics (LA) provides an opportunity to address these societal challenges. As the LA field matures, tool development is aimed at aiding informed human decision-making and combating inequalities. For example, detecting students at risk of dropping out or supporting self-regulated learning. The inception of LA was catalysed by an increasing amount of available data and what could be done with these data to improve learner support and teaching. Simultaneously, an increase in the computational power, machine learning methods, and tools at hand offer renewing affordances to analyse and visualise data both retrospectively and for predictive purposes. Employing LA as a solution also brings potential problems, such as unequal treatment, privacy concerns, and unethical practices. Through selected example cases, this chapter presents and addresses these potentials and risks

A typology of teachers’ self-reported use of student data from computer-based assessment programmes in secondary education

Teachers in secondary education have to deal with a growing diversity in student population, which asks for differentiation of their teaching. Computer-based assessments (CBA’s) are educational software tools that, for a particular school subject, allow students to practice their knowledge or skills, leading to results about students’ performance or ability level at that point in time. Information derived from CBA’s (known as learning analytics) has the potential to support teachers to differentiate by informing them of each student’s current level. In this study, our aim was to provide a typology of how and why teachers in secondary education use analytics, with the subsequent aim of providing recommendations for teacher professional development. Four profiles were found: high users, selective users, early stage users, and non-users. We provide recommendations for each of these profiles.publishedVersio