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Abstract.Educational institutions are designing, creating and evaluating courses to optimize learning outcomes for highly diverse student populations. Yet, most of the delivery is still monitored retrospectively with summative evaluation forms. Therefore, improvements to the course design are only implemented at the very end of a course, thus missing to benefit the current cohort. Teachers find it difficult to interpret and plan interventions just-in-time. In this context, Learning Analytics (LA) data streams gathered from 'authentic' student learning activities, may provide new opportunities to receive valuable information on the students' learning behaviors and could be utilised to adjust the learning design already "on the fly" during runtime. We presume that Learning Analytics applied within Learning Design (LD) and presented in a learning dashboard provide opportunities that can lead to more personalized learning experiences, if implemented thoughtfully.In this paper, we describe opportunities and challenges for using LA in LD. We identify three key opportunities for using LA in LD: (O1) using on demand indicators for evidence based decisions on learning design; (O2) intervening during the run-time of a course; and, (O3) increasing student learning outcomes and satisfaction. In order to benefit from these opportunities, several challenges have to be overcome. We mapped the identified opportunities and challenges in a conceptual model that considers the interaction of LA in LD.

Plain English summaryParents of children with physical disabilities do a lot to support their child in daily life. In doing this they are faced with many challenges. These parents have a wide range of unmet needs, especially for information, on different topics. It is sometimes hard for them to get the right information at the right moment, and to ask the right questions to physicians and other healthcare professionals. In order to develop a digital tool to help parents formulate questions and find information, we thought it would be crucial to work together in a process of co-creation with parents, researchers, IT-specialists and healthcare professionals. In close collaboration with them we developed a tool that aims to help parents ask questions, find information and take a more leading role in consultations with healthcare professionals, called the WWW-roadmap (WWW-wijzer in Dutch).In two groups of parents (one group with and one group without experience of using the tool), we will study the effects of using this tool, on consultations with physicians. We expect that using the tool will result in better empowerment, satisfaction and family-centred care.AbstractBackgroundParents of children with physical disabilities do much to support their child in daily life. In doing so, they are faced with many challenges. These parents have a wide range of unmet needs, especially for information, on various topics. Getting timely and reliable information is very difficult for parents, whereas being informed is a major requirement for the process of empowerment and shared decision-making. This paper describes the development of a digital tool to support parents in this process. During its development, working together with parents was crucial to address relevant topics and design a user-centred intervention.MethodsIn co-creation with parents, healthcare professionals, IT-professionals and researchers, a digital tool was developed, the ‘WWW-roadmap’ [‘WWW-wijzer’ in Dutch]. This digital tool aims to enable parents to explore their questions (What do I want to know?), help in their search for information (Where can I find the information I need), and refer to appropriate professionals (Who can assist me further?).During the process, we got extensive feedback from a parent panel consisting of parents of children with physical disabilities, enabling us to create the tool ‘with’ rather than ‘for’ them. This led to a user-friendly and problem-driven tool.DiscussionThe WWW-roadmap can function as a tool to help parents formulate their questions, search for information and thus prepare for consultations with healthcare professionals, and to facilitate parental empowerment and shared-decision making by parent and professional. Effects of using the WWW-roadmap on consultations with professionals will be studied in the future.

For decades, self-report instruments-which rely heavily on students' perceptions and beliefs-have been the dominant way of measuring motivation and strategy use. Eventbased measures based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance. The results also reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace data, likely because of its covert nature. In that respect, it is of importance to recognize the crucial role of self-reports in capturing student learning holistically. This manuscript is 'frontline' in the sense that event-based measurement methodologies with online trace data are relatively unexplored. The comparison with self-report data made in this manuscript sheds new light on the added values of innovative and traditional methods of measuring motivation and strategy use.

Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.