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Background: Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives: This paper focuses on instructor-led feedback mediated by LA tools in MOOCs. Our goal is to answer how, to what extent data-driven feedback is provided to learners, and what its impact is. Methods: We conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, we selected 38 articles that address the topic of LA-informed feedback in MOOCs mediated by instructors. We applied etic content analysis to the collected data. Results and Conclusions: The results revealed a lack of empirical studies exploring LA to deliver feedback, and limited attention on pedagogy to inform feedback practices. Our findings suggest the need for systematization and evaluation of feedback. Additionally, there is a need for conceptual tools to guide instructors' in the design of LA-based feedback. Takeaways: We point out the need for systematization and evaluation of feedback. We envision that this research can support the design of LA-based feedback, thus contributing to bridge the gap between pedagogy and data-driven practice in MOOCs.

Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative impact (e.g., on students, academic stakeholders, and institutions). However, it is not clear how institutional stakeholders can integrate IA in their practice to overcome academic-related issues and to offer support to students who struggle to achieve their academic goals. To address this gap, we conducted focus groups with 13 institutional stakeholders of an Estonian university. By analyzing the focus group data, we identified three main categories of factors influencing dropout from the perspective of institutional stakeholders: (1) institutional experience, (2) educational goals, and (3) personal aspects. We discuss our findings from an institutional perspective with the aim of reflecting on institutional processes, organizational structures, and facilitatory roles in the context of dropout in higher education (HE). We argue that IA can provide insights into students' institutional experience, educational goals, and personal aspects to further support decision-making on the institutional level. We envision our findings contributing to a participatory agenda for the design, implementation, and integration of IA solutions focusing on addressing dropout in HE.

The potential of social media technology has made its use a daily habit among individuals, institutions, and communities. However, several studies on technology adoption, especially social media use in education, focus more on its impact on the student than the teacher, who is generally perceived as a key stakeholder. This study used purposive sampling to select teachers who taught grades 7-10 and had used social media in their teaching activities. In-depth interviews were carried out with participating teachers to get their opinions and perspectives about how they used social media in their teaching activities (N=11). Inductive and deductive coding were used for the latent content analysis and four categories emerged: (1) SM technology in the classroom, (2) positive perceived contextual affordances, (3) negative perceived contextual affordances, and (4) support for social media. Results of the study show that, besides the schools' learning management systems, YouTube was the major SM app that was regularly used by participants in their lessons. Also, all participating teachers expressed their interest in teaching with social media. However, they cited some challenges as weaknesses towards social media use in teaching.

This paper presents the findings of a study into the application process of the professional standards (PS) qualification in Estonia, which is a standard used to identify the level of professionalism of a teacher. The study collected the views of active teachers who have applied for the PS and those of the assessors to understand the shortcomings in the application and the expectations for the application process. The main challenges identified are that teachers lack respective skills to adequately comprehend qualification requirements (unambiguous interpretation of PS) and the lack of evidence-based performance indicators (difficult to prove) and weak self-analysis skills; hence, the teacher needs assistance. The research found that teachers need collaborative support, motivation and constructive feedback on their professional development (PD), both from colleagues and at school and national levels. Based on this research, it can be said that teachers who have gone through the application process have gained greater self-confidence, value themselves as teachers and continue their in-service training.

In this article, we present a study on teachers' perceptions about Artificial Intelligence (AI) as a tool to support teaching in Estonian K-12 education. Estonia is promoting technological innovation in education. According to the Index of Readiness for Digital Lifelong Learning (IRDLL), Estonia was ranked first among 27 European countries. In this context, our goal was to explore teachers' perceptions about cutting-edge technologies (in this case, AI) and to contextualize our results in the scope of Fairness, Accountability, Transparency and Ethics (FATE). We carried out a survey with 140 Estonian K-12 teachers and we asked them about their understanding and concerns regarding the use of AI in education and the challenges they face. The analysis of the survey responses suggests that teachers have limited knowledge about AI and how it could support them in practice. Nonetheless, they perceive it as an opportunity for education. The results indicate that teachers need support in order to be efficient and effective in their work practice; we envision that AI can be used to provide this support. Furthermore, we identified challenges that relate to the socio-cultural context of the study: for example, teachers perceived AI as a tool to support them in accessing, adapting and using multilingual content. To conclude, we discuss the findings of this work in relation to ethical AI, and elaborate on the implications and future aspects of this work in the context of FATE and participatory design of learning environments.

Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However, considerably less attention has been paid to using a student model to personalize instruction in tutorial dialogue systems (TDSs)--ITSs that engage students in natural-language, conceptual discussions. This paper describes Rimac, a TDS that tightly couples student modelling with tutorial dialogues about conceptual physics. Rimac is distinct from other TDSs insofar as it dynamically builds a persistent student model that guides reactive and proactive decision making in order to provide adaptive instruction. An initial pilot study set in high school physics classrooms compared a control version of Rimac without a student model with an experimental version that implemented a "poor man's student model"; that is, the model was initialized using students' pretest scores but not updated further. Both low and high prior knowledge students showed significant pretest to posttest learning gains. However, high prior knowledge students who used the experimental version of Rimac learned more efficiently than high prior knowledge students who used the control version. Specifically, high prior knowledge students who used the student model driven tutor took less time to complete the intervention but learned a similar amount as students who used the control version. A subsequent study found that both high and low prior knowledge students learned more efficiently from a version of the tutor that dynamically updates its student model during dialogues than from a control version that included the static "poor man's student model." We discuss future work needed to improve the performance of Rimac's student model and to integrate TDSs in the classroom.

To further develop rich and expressive ways of modelling roles of contributors in discussion forums of online courses, particularly in MOOCs, networks of forum users are analyzed based on the relations of information-giving and information-seeking. Specific connection patterns that appear in the information exchange networks of forum users are used to characterize user roles. Additionally, semantic roles are derived by identifying thematic areas in which an actor (learner) looks for information (problem areas) and the areas of interest in which an actor provides information to others (areas of expertise). The interplay of social and semantic roles is analyzed using a socio-semantic blockmodelling approach. The results indicate that social and semantic roles are not strongly interdependent. The methodological contribution is in combining traditional blockmodelling with semantic information to characterize participant roles. Furthermore, we use sequential pattern analysis techniques to analyze the posting activity of users over time in terms of categories of cognitive engagement. The combination of the different approaches reveals that user roles derived from the analysis of engagement patterns are strongly related to socio-semantic user roles. [Paper presented at the LAK '16--International Conference on Learning Analytics and Knowledge (6th, Edinburgh, United Kingdom, April 25-29, 2016).]

In this work, we theorise the role of the educational technologist in an educational institution as a "variety-handler". That is, we theorise the educational technologist as responsible for the orchestration of educational and technical resources, namely tools, either to attenuate systemic variety or amplify regulative variety at a certain level of recursion. To do so, we carried out a single case study that focused on the work of the educational technologist in a Music College. The educational technologist provided support to an accordion teacher and his student in remote music lessons. We collected qualitative data from the practice of the educational technologist and the experience of both the teacher and the student over six months (mainly observations and interviews) and we analyzed them building on research from managerial cybernetics, in particular, Stafford Beer's Viable System Model (VSM). Our case study indicates that, depending on the context, the educational technologist may overtake many roles, such as be a designer, technician, researcher, planner and so on. But the central role is that of handling variety in order to avoid disruption that could potentially affect the lesson negatively.

By using standardised approaches, systematic reviews of the educational, scientific literature can inform educational research and influence educational policies and practices. However, the various systematic reviews of the scientific literature in the field of Artificial Intelligence (AI) and education all adopt individual approaches, making it challenging to systematically compare their conclusions. Accordingly, this paper presents a standardised protocol for conducting systematic reviews of the scientific literature on AI and education (AI&ED), including both literature on teaching and learning with AI (AIED) and literature on teaching and learning about AI (AI literacy). Our protocol applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and is presented here for the purpose of replication and validation. We exemplify our protocol by means of a systematic review of the scientific literature on trustworthy and ethical AI&ED, which was undertaken iteratively in symbiosis with the development of the protocol, informing each other throughout. In the future, we intend to apply our novel protocol for other search terms of relevance to AI&ED, as well as for the same search terms over a longer time period, in order to allow comparisons and the exploration of trends. [ABSTRACT FROM AUTHOR]