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Feedback is essential in learning. The emerging concept of feedback literacy underscores the skills students require for effective use of feedback. This highlights students' responsibilities in the feedback process. Yet, there is currently a lack of mechanisms to understand how students make sense of feedback and whether they act on it. This gap makes it hard to effectively support students in feedback literacy development and improve the quality of feedback. As a specific application of learning analytics, feedback analytics (analytics on learner engagement with feedback) can offer insights into students' learning engagement and progression, which can in turn be used to scaffold student feedback literacy. This study proposes a feedback analytics tool, designed with students, aimed at aiding students to synthesize feedback received from multiple sources, scaffold the sense-making process, and prompt deeper reflections or actions on feedback based on data about students' interactions with feedback. We held focus group discussions with 38 students to learn about their feedback experiences and identified tool features. Based on identified user requirements, a prototype was developed and validated with 16 students via individual interviews. Based on the findings, we envision a feedback analytics tool with the aim of scaffolding student feedback literacy.

Educational systems generate huge quantities of digital data. Digital educational data is captured and used at all points -- from classrooms and schools, to the level of educational departments. As growing trends in ‘data-driven instruction’ suggest, all these data have great potential to support student, teacher and leadership practices, help guide work and learning decisions, and inform policy development. Moreover, an increasing focus is being placed on the development of artificial intelligence to automate and improve how data are used. Yet, stakeholder data practices remain invisible and little understood, which complicates how artificial intelligence can be embedded in this context. In this paper, we introduce an educational data journeys framework to frame dynamics of data power, data work, identities and literacies. This approach is employed to explore educational policy revealing data flows and frictions in school improvement and implications for the development of artificial intelligence in education.

BackgroundThere is an increasing amount of electronic data sitting within the health system. These data have untapped potential to improve clinical practice if extracted efficiently and harnessed to change the behavior of health professionals. Furthermore, there is an increasing expectation from the government and peak bodies that both individual health professionals and health care organizations will use electronic data for a range of applications, including improving health service delivery and informing clinical practice and professional accreditation. ObjectiveThe aim of this research program is to make eHealth data captured within tertiary health care organizations more actionable to health professionals for use in practice reflection, professional development, and other quality improvement activities. MethodsA multidisciplinary approach was used to connect academic experts from core disciplines of health and medicine, education and learning sciences, and engineering and information communication technology with government and health service partners to identify key problems preventing the health care industry from using electronic data to support health professional learning. This multidisciplinary approach was used to design a large-scale research program to solve the problem of making eHealth data more accessible to health professionals for practice reflection. The program will be delivered over 5 years by doctoral candidates undertaking research projects with discrete aims that run in parallel to achieving this program’s objectives. ResultsThe process used to develop the research program identified 7 doctoral research projects to answer the program objectives, split across 3 streams. ConclusionsThis research program has the potential to successfully unpack electronic data siloed within clinical sites and enable health professionals to use them to reflect on their practice and deliver informed and improved care. The program will contribute to current practices by fostering stronger connections between industry and academia, interlinking doctoral research projects to solve complex problems, and creating new knowledge for clinical sites on how data can be used to understand and improve performance. Furthermore, the program aims to affect policy by developing insights on how professional development programs may be strengthened to enhance their alignment with clinical practice. The key contributions of this paper include the introduction of a new conceptualized research program, Practice Analytics in Health care, by describing the foundational academic disciplines that the program is formed of and presenting scientific methods for its design and development. International Registered Report Identifier (IRRID)PRR1-10.2196/27984

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a well-performed ML model is nontrivial since the sample size is commonly limited for model training. Based on this fact, we posited that the model performance can be further optimized by the design of input features and the selection of informative instances for model training. By reviewing the existing dialogue-related research, we found that contextual information (i.e., the content of preceding utterances) was a potential feature in many text classification tasks but underexplored in the classification of an instructional strategy. In addition, training a well-performed ML model (e.g., deep neural network) to recognize instructional strategies usually requires a large amount of manually annotated data, which are labor intensive. To alleviate the demand of manual annotation, researchers proposed the use of statistical active learning methods to select informative instances, but this method was rarely used for recognizing the instructional strategy in online tutoring dialogue. Therefore, our study aimed to investigate the improvement of automating the classification of dialogue acts--a popular approach to the detection of instructional strategies--from two perspectives. First, we explored whether and to what extent the incorporation of contextual information can boost a model's prediction performance. Then, we investigated the extent to which the recent active learning methods can alleviate the labor-intensive issues in training the ML model for recognizing the instructional strategies. Our study showed that: 1) the ML models trained on the features that included the contextual information achieved better performance than that of the models excluding it; 2) the effectiveness of the contextual information decayed after the ML model achieved an optimal performance; and 3) compared with the random baseline, active learning methods can select informative samples from the training dataset to train ML models, which can alleviate the labor-intensive issues.

Dual-layer MOOCs are an educational framework designed to create customizable modality pathways through a learning experience. The basic premise is to design two framework choices through a course - one that is instructor guided and the other that is student-determined and open. Learners have the option to create their own customized pathway by choosing or combining both modalities as they see fit at any given time in the course. This mixed-methods study sought to understand the patterns that learners engaged in during a course designed with this pathway framework. The results of the quantitative examination of the course activity are presented, as well as the categories and themes that arose from the qualitative research. The results of the analysis indicates that learners value the ability to choose the pathway that they engage the course in. Additional research is needed to improve the technical and design aspects of the framework.

This paper reports on the results of an analysis of the research proposals submitted to the MOOC Research Initiative (MRI) funded by the Gates Foundation and administered by Athabasca University. The goal of MRI was to mobilize researchers to engage into critical interrogation of MOOCs. The submissions – 266 in Phase 1, out of which 78 was recommended for resubmission in the extended form in Phase 2, and finally, 28 funded – were analyzed by applying conventional and automated content analysis methods as well as citation network analysis methods. The results revealed the main research themes that could form a framework of the future MOOC research: i) student engagement and learning success, ii) MOOC design and curriculum, iii) self-regulated learning and social learning, iv) social network analysis and networked learning, and v) motivation, attitude and success criteria. The theme of social learning received the greatest interest and had the highest success in attracting funding. The submissions that planned on using learning analytics methods were more successful. The use of mixed methods was by far the most popular. Design-based research methods were also suggested commonly, but the questions about their applicability arose regarding the feasibility to perform multiple iterations in the MOOC context and rather a limited focus on technological support for interventions. The submissions were dominated by the researchers from the field of education (75% of the accepted proposals). Not only was this a possible cause of a complete lack of success of the educational technology innovation theme, but it could be a worrying sign of the fragmentation in the research community and the need to increased efforts towards enhancing interdisciplinarity.