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Artificial Intelligence (AI) is widely used for the assessment of multiple-choice questions. There is an increasing effort to also use it for open-ended questions. While the use of AI can benefit the learning of students, e.g. by increasing the number of feedback moments, most applications focus on saving costs by reducing the need for manual assessment. The perspective of teachers on this kind of automation has been studied extensively, the student perspective, however, is still under-researched. This paper presents the results of two surveys and a series of interviews among students to identify their perspective on AI-supported assessment and elaborate on under which conditions they would accept such technology. The results show that the majority of students (more than 80%), is, under certain conditions, open to AI-supported assessment. Most importantly, they stress that humans should still be involved in the assessment (human-in-the-loop).

Producción Científica, Background:Providing feedback in massive open online courses (MOOCs) is chal-lenging due to the massiveness and heterogeneity of learners' population. Learninganalytics (LA) solutions aim at scaling up feedback interventions and supportinginstructors in this endeavour.Paper Objectives:This paper focuses on instructor-led feedback mediated by LAtools in MOOCs. Our goal is to answer how, to what extent data-driven feedback isprovided 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, weselected 38 articles that address the topic of LA-informed feedback in MOOCs medi-ated by instructors. We applied etic content analysis to the collected data.Results and Conclusions:The results revealed a lack of empirical studies exploring LA todeliver feedback, and limited attention on pedagogy to inform feedback practices. Our find-ings suggest the need for systematization and evaluation of feedback. Additionally, there isa 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. Weenvision that this research can support the design of LA-based feedback, thus contribut-ing to bridge the gap between pedagogy and data-driven practice in MOOCs., Consejo de Investigación de Estonia (PSG286), Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional y la Agencia Nacional de Investigación (grant PID2020-112584RB-C32) and (grant TIN2017-85179-C3-2-R), Junta de Castilla y León - Fondo Social Europeo y el Consejo Regional de Educación (grant E-47-2018-0108488)

From June 22 to June 24, 2021, Hasso Plattner Institute, Potsdam, hosted the seventh European MOOC Stakeholder Summit (EMOOCs 2021) together with the eighth ACM Learning@Scale Conference. Due to the COVID-19 situation, the conference was held fully online. The boost in digital education worldwide as a result of the pandemic was also one of the main topics of this year’s EMOOCs. All institutions of learning have been forced to transform and redesign their educational methods, moving from traditional models to hybrid or completely online models at scale. The learnings, derived from practical experience and research, have been explored in EMOOCs 2021 in six tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference’s Experience Track, the Policy Track, the Business Track, the International Track, and the Workshops.

Providing adequate support to MOOC participants is often a challenging task due to massiveness of the learners’ population and the asynchronous communication among peers and MOOC practitioners. This workshop aims at discussing common learners’ problems reported in the literature and reflect on designing adequate feedback interventions with the use of learning data. Our aim is three-fold: a) to pinpoint MOOC aspects that impact the planning of feedback, b) to explore the use of learning data in designing feedback strategies, and c) to propose design guidelines for developing and delivering scaffolding interventions for personalized feedback in MOOCs. To do so, we will carry out hands-on activities that aim to involve participants in interpreting learning data and using them to design adaptive feedback. This workshop appeals to researchers, practitioners and MOOC stakeholders who aim to providing contextualized scaffolding. We envision that this workshop will provide insights for bridging the gap between pedagogical theory and practice when it comes to feedback interventions in MOOCs.

Research on pedagogical agents (PAs) focuses on personalizing and adapting content and instruction to students’ diverse needs to support learning. Teachers can use this technology to support individual students’ work. However, it is not clear what could be the impact of a PA that helps teachers to orchestrate collaborative learning activities on the classroom level. Our work explores two dimensions. Firstly, the effects of employing a PA in a technology-enhanced learning setting to promote students’ motivation and learning outcomes. To that end, we will conduct a series of studies employing various methods and data (tests, questionnaires, observations, and students’ performance data). Secondly, this work aims to develop a design framework based on teachers’ expectations and needs when using a PA to orchestrate collaborative learning tasks. To build the PA design framework, we will conduct a study to categorize teachers’ PA expectations and needs, accompanied with findings from the literature. Our hypothesis is that classrooms, where the PA is used to support teachers in the learning activity, will demonstrate high learning gains and students’ perceived motivation.

International audience; There is an increasing interest in student-centered teaching methods with smallgroup learning as an important ingredient. In this paper, we present a study in which theperformance of heterogeneous and homogeneous learning groups has been compared in atechnology-enhanced classroom setting in the area of STEM learning. The group formationwas based on learning analytics results that were considered in a semi-automatic formationprocess. The analytic methods used incorporated different artefact-related characteristics, butalso motivational features as input. We observed that the heterogeneous groups outperformedthe homogeneous ones in different ways. The results of the study are analysed usingquantitative and qualitative approaches on both the individual and the group level.

At an earlier stage of European funding for projects on technology-enhanced learning, the main thrust was to develop e-learning technologies and on projects that sought to promote the take-up of platforms and services. This contribution is prepared by students after attending lectures of a multidisciplinary course in the context of a complementary studies frame. The students of this course summarized, through a case study, concepts and methods in a straightforward, but structured way. Thanks to the help of a software tool based on Python, an original and open learning platform is realized for students and lecturers and it represents a part of this contribution. Concerning the specific lecture, cargo loads and transportation are important logistical topics in many industries. They dictate the profit of the final product, expenditure, time consumption and labor force utilization. The optimal transportation of any type of cargo is crucial for businesses. More in general, it is possible to say that the proposed problem can be generalized and applied in other main economic problems in which optimization problems are involved. In this work we focused on the optimization of fluid transportation under specified conditions, or constraints, in other words. The aim of the project is to determine the optimal parameters of the system to control the transportation in an optimal way. This material which includes an open software to test the developed concepts through the lecture can be used by students and lecturers. An open link is accessible to the users.

The enrollment of computer science students continues to increase, with record enrollment numbers in the course ”Data Structures and Algorithms” in fall 2022. As a result, teaching methods and systems must evolve to support student progress in the face of scarce teaching resources. This paper examines the shift from manual to peer-reviewed and auto-graded grading processes and investigates students’ perceptions of different grading styles. The developed automatic graders utilize data from both the Plussa LMS and GitLab, which serves as the channel for student submissions and provides feedback for formative assessments. The results indicate that peer-reviews are accepted as an exercise for the reviewer, but not as a means to grade the reviewee. Autograders are well-received due to their instant feedback, ability to allow for multiple submissions and iterate towards more efficient solutions, which helps foster a growth mindset. publishedVersion

https://www.ester.ee/record=b5366756*est

https://www.ester.ee/record=b5366850*est

Creativity is widely recognized as a valuable trait. The aim of this research is to explore the usefulness of incorporating elements of both game and play into creativity methods, so called design games or gameful ideation methods. This paper presents findings from a study in which a Gameful Idea Generation (GIG) was conducted. GIG is a collaborative creativity method that draws on concepts from design games and hackathons. We focus on intra-group dynamics in order to gain insights on how gamefulness influences collaborative creative processes. We analyse performance in relation to the level and nature of collaboration displayed during group-based ideation. Our insights provide relevant pointers for the design of future collaborative creativity enhancing technologies.

Η διδακτορική διατριβή εντάσσεται στο πεδίο της Υπολογιστικά Υποστηριζόμενης Συνεργατικής Μάθησης, ΥΥΣΜ (Computer Supported Collaborative Learning, CSCL). ‘Eχει ως στόχο την ανάπτυξη και πρόταση μίας μεθόδου για την αυτοματοποιημένη ανάλυση, ταξινόμηση και αξιολόγηση της ποιότητας συνεργατικών εκπαιδευτικών δραστηριοτήτων. Αφενός βασίζεται σε ευρήματα ποιοτικής έρευνας και αφ’ ετέρου συνδυάζει την χρήση τεχνικών μηχανικής μάθησης και ανώτερων μαθηματικών που χρησιμοποιούνται ευρέως σε πλήθος άλλων ερευνητικών πεδίων, μελετώντας τους τρόπους που μπορούν να υιοθετηθούν και να συνεισφέρουν στο πεδίο της Υπολογιστικά Υποστηριζόμενης Συνεργατικής μάθησης (χρονοσειρές). Βασικό μέλημα είναι η προτεινόμενη μέθοδος να επιτρέπει την εξαγωγή χρήσιμων συμπερασμάτων για την ποιότητα των συνεργατικών δραστηριοτήτων με τρόπο ποσοτικό και αυτόματο ώστε να είναι δυνατή η χρήση της σε μεγάλα σύνολα δεδομένων. Η παρούσα μελέτη έδειξε πως η ποιότητα της συνεργασίας αποτυπώνεται στον τρόπο που κατανέμεται η συνεργατική δραστηριότητα στον χρόνο και η χρήση χρονοσειρών αποτυπώνει τα ιδιαίτερα χαρακτηριστικά της συνεργασίας με ικανοποιητικό τρόπο. Η μέθοδος αξιολογήθηκε ξεχωριστά αλλά και σε αντιπαράθεση με αντίστοιχα μοντέλα και μεθόδους. Η προτεινόμενη μέθοδος πλεονεκτεί ως προς την απλότητα κατασκευής και λειτουργίας ενώ διαπιστώθηκε με στατιστικά σημαντικό τρόπο η εγκυρότητα των αποτελεσμάτων της. H μέθοδος δεν απαιτεί την ολοκλήρωση της δραστηριότητας αλλά ενδείκνυται και για την αξιολόγησή της σε πραγματικό χρόνο. Οι χρονοσειρές δραστηριότητας περιγράφουν ικανοποιητικά βασικές συνεργατικές διαστάσεις που ορίζονται ως «χαμηλού επιπέδου» όπως η Επικοινωνία και η Κοινή Επεξεργασία Πληροφορίας και οι οποίες θεωρείται ότι αντικατοπτρίζονται ιδιαίτερα στην διαλογική δραστηριότητα που εκτυλίσσεται μεταξύ των συνεργατών. Από την άλλη, για διαστάσεις ανωτέρου επιπέδου που αντιπροσωπεύονται από πιο περίπλοκες δομές αλληλεπίδρασης, όπως για παράδειγμα ο Συντονισμός και η Διαπροσωπική Σχέση μεταξύ συνεργαζόμενων μερών, οι χρονοσειρές δραστηριότητας δεν καταφέρνουν να τις αποτυπώσουν ικανοποιητικά. Αποδείχθηκε στατιστικά πως για την συγκεκριμένη περίπτωση συνεργατικών μαθησιακών δραστηριοτήτων οι ουσιαστικές αλληλεπιδράσεις μεταξύ των χρηστών που υποδηλώνουν μεταφορά και οικοδόμηση κοινής γνώσης και κοινού τόπου επικοινωνίας αλλά και σχέσεις αιτιότητας, είναι δυνατόν να ανιχνευθούν μέσα σε χρονικά παράθυρα μικρού μεγέθους, της τάξης των τριάντα δευτερολέπτων. Σύνθετες δομές που αποτυπώνουν την δημιουργία στρατηγικής προσέγγισης ή διαμοιρασμού χώρου και χρόνου και απαιτούν μεγαλύτερους χρόνους εξέλιξης, δεν είναι δυνατόν να αποτυπωθούν επαρκώς με αυτή την προσέγγιση. Η έρευνα αυτή δεν αποσκοπεί κατά κανένα τρόπο στην αντικατάσταση της ανθρώπινης κρίσης ή γενικότερα του ανθρώπινου παράγοντα αλλά αντίθετα επιδιώκει να υποστηρίξει το έργο του. The PhD thesis is part of ongoing research in the field of Computer-Supported Collaborative Learning (CSCL). The main contribution of this thesis is to design and propose a method for the automatic analysis, classification and evaluation of the quality of collaboration of learning activities. On one hand, the method is based and reflects the findings of qualitative research and on the other hand, it uses machine learning algorithms and statistical methods that allow the quantitative analysis of data. We used modeling techniques widely used in other scientific fields (time series) and studied how they can be used in CSCL to contribute new knowledge. The objective of the study is to implement a method for the representation, classification and evaluation of collaborative activities. It was shown that the quality of collaboration and its fundamental aspects is portrayed in the way the activity itself is distributed in time. It was shown through visualizations and statistical analysis that time series allow the effective representation of collaboration and its qualitative characteristics. The classification and evaluation method that was proposed is supported by a machine-learning model. The model was further evaluated as an automated rater of collaboration quality and compared to other similar models. The advantage of the proposed method over others is the simple structure and low-cost, as well as the potential to be used in real-time. The proposed approach attempts to describe and portray the interaction of users through their concurrent activity on different but common workspaces. For that reason we make use of common, basic activity metrics and time series. The time series of activity can describe successfully low level construct such as Communication and Information Processing. For more advanced and complicated constructs however, such as Coordination and Interpersonal Relationship, time series could not capture adequately the qualitative characteristics and underlying mechanisms. This finding comes in agreement with similar studies that point out the need of combined analysis methods that will use in combination content analysis techniques and natural language processing. It was also shown that in the particular context, the meaningful interactions that point to constructive collaboration, successful knowledge building and reciprocal activity can be mapped in small time frames, of about 30 seconds. More complicated structures that signify e.g. strategy planning and effective coordination, take more time to unfold and therefore cannot be traced in such small time frames. This study does not attempt in any way to substitute or overcome the human judgment and human factor, either in the analysis or teaching activity. On the contrary, we believe that the teacher cannot be replaced by automated tools and methods but should be supported and empowered.