Peer-Allocated Instant Response (PAIR): Computational Allocation of Peer Tutors in Learning Communities

This paper proposes a computational model for the allocation of fleeting peer tutors in a community of learners: a student\'s call for support is evaluated by the model in order to allocate the most appropriate peer tutor. Various authors have suggested peer tutoring as a favourable approach for confining the ever-growing workloads of teachers and tutors in online learning environments. The model\'s starting point is to serve two conflicting requirements: 1) the allocated peers should have sufficient knowledge to guarantee high quality support and 2) tutoring workload of peers should be fairly distributed over the student population. While the first criterion is likely to saddle a small number of very bright students with all the tutoring workload, the unconditional pursuit of a uniform workload distribution over the students is likely to allocate incompetent tutors. In both cases the peer support mechanism is doomed to failure. The paper identifies relevant variables and elaborates an allocation procedure that combines various filter types. The functioning of the allocation procedure is tested through a computer simulation program that has been developed to represent the student population, the students curriculum and the dynamics of tutor allocation. The current study demonstrates the feasibility of the self-allocating peer tutoring mechanism. The proposed model is sufficiently stable within a wide range of conditions. By introducing an overload tolerance parameter which stretches the fair workload distribution criteria, substantial improvements of the allocation success rate are effected. It is demonstrated that the allocation algorithm works best at large population sizes. The results show that the type of curriculum (collective route or individualised routes) has only little influence on the allocation mechanism.Distance Learning, Computational Simulations, System Dynamics, Education and Application, Peer Support, Peer Allocation

Don't Blame Distributional Semantics if it can't do Entailment

Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on Computational Semantics (IWCS 2019), Gothenburg, Swede

Technology-Enhanced Learning: Review and Prospects

This paper is a reflection on the history and future of technology-enhanced learning. Over the last century various new technologies were introduced in education. Often, educational revolutions were proclaimed. Unfortunately, most of these new technologies failed to meet the high expectations. This paper reviews the rise and fall of various "revolutionary" learning technologies and analyses what went wrong. Three main driving factors are identified that influence the educational system: 1) educational practice, 2) educational research, and 3) educational technology. The role and position of these factors is elaborated and critically reviewed. Today, again many promising new technologies are being put in place for learning: gaming, social web, and mobile technologies, for example. Inevitably, these are once again proclaimed by its supporters to revolutionise teaching and learning. The paper concludes with identifying a number of relevant factors that substantiate a favourable future outlook of technology-enhanced learning

Beyond functionality and technocracy: creating human involvement with educational technology

Westera, W. (2005). Beyond functionality and technocracy: creating human involvement with educational technology. Educational Technology & Society, 8(1), 28-37.Innovation of education is highly topical. It is obviously boosted by a range of new technologies, which enable new modes of learning that, are independent of time and place through Web-based delivery and computer-mediated communication. However, innovators in education often encounter intrinsic conservatism or even deliberate obstructions. For innovators it is important to be aware of and to understand the basic premises underlying the idea of innovation. This paper explains the origins of technological optimism and the associated faith in progress. Also, techno-pessimism as rooted in the negative side effects of the industrial revolution is reviewed. To solve the conflict between techno-optimism and techno-pessimism we elaborate Borgmann’s “devices paradigm“: in order to avoid apathetic and indifferent consumption of technology-based commodities, users of technological devices should be given the opportunity to develop substantial involvement with the technological devices. While extending this idea to educational technologies, we present an explanatory model for the mediating role of technological artefacts. In conclusion, we explain how to approach technology-based innovations in education by arguing for transparent and interactive devices, for products as carriers of meaning, for values that harmonise with the characteristics of man and for a mixed mode of developing new ideas and preserving former achievements

How people learn while playing serious games: A computational modelling approach

This paper proposes a computational modelling approach for investigating the interplay of learning and playing in serious games. A formal model is introduced that allows for studying the details of playing a serious game under diverse conditions. The dynamics of player action and motivation is based on cognitive flow theory, which is expressed in quantitative terms for this purpose. Seven extensive simulation studies involving over 100,000 iterations have demonstrated the stability of the model and its potential as a research instrument for serious gaming. The model allows researchers to deeply investigate quantitative dependencies between relevant game variables, gain deeper understanding of how people learn from games, and develop approaches to improving serious game design.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains