Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

People can learn a lot through (collaborative) reflection at work: In organizations, staff debate experiences and due to issues every day, thus reflecting together and learning from each other. While this is desirable, it is often hindered by differences in time and space. Online discussions in community-like systems may provide a means to overcome this issue and enable staff to share experiences and learn from them. In this paper, we study two different data sets from two software systems to support online collaborative reflection in order to analyze what possible factors influence the occurrence of aspects of reflection in online discussions, like experiences, suggestions and learning. Our results include findings that partially approve existing models of (collaborative) reflection, but also others that add to or even challenge these models. Overall, we found that collaborative reflection may take different paths towards learning. From this we derive that facilitation mechanisms need to take up these paths, and that facilitation may be successful in different ways than anticipated from existing literature. We describe possible facilitation mechanisms and discuss their implementation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dev Camps are events that enable participants to tackle challenges using software tools and different kinds of hardware devices in collaborative project-style activities. The participants conceptualize and develop their solutions in a self-directed way, involving technical, organizational and social skills. In this sense, they are autonomous producers or 'makers'. The Dev Camp activity format resonates with skills such as communication, critical thinking, creativity, decision-making and planning and can be considered as a bridge between education and industry. In this paper we present and analyse our experience from a series of such events that were co-organized between an industrial partner acting as a host and several university partners. We take this as an indication to envision new opportunities for project-based learning in more formal educational scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the integration of a real time evaluation method of collaboration quality in a monitoring application that supports teachers in class orchestration. The method is implemented as an automatic rater of collaboration quality and studied in a real time scenario of use. We argue that automatic and semi-automatic methods which provide assessments of collaboration quality or detect disorders and anomalies in collaborative activities should be further used to assist class monitoring. To that end, we study the routine of teachers when they monitor computer-supported collaborative activities with respect to the size of the classroom and the support of an automatic rater. We focus on the way the rater affects their task and how it can be further used to empower the practice of teachers. [ABSTRACT FROM AUTHOR]

After the publication of this work [1] an error was noticed in the title of the article. The title in the original article reads: 'From Making to Learning': introducing Dev Camps as an educational paradigm for Re-inventing Problem-based Learning. However the correct article title should read: 'From Making to Learning': introducing Dev camps as an educational paradigm for Re-inventing Project-based Learning. This has been corrected here. [ABSTRACT FROM AUTHOR]