Machine and social intelligent peer-assessment systems for assessing large student populations in massive open online education

The motivation of the European Etoile project is to create high quality free open education in complex systems science, including quality assured certification. Universities and colleges around the world are increasingly using online platforms to offer courses open to the public. Massive Open Online Courses or MOOCs give millions of people access to lectures delivered by prestigious universities. However, although some of these courses provide certification of attendance and completion, most do not provide any academic or professional recognition since this would imply a rigorous and complete evaluation of the student’s achievements. Since the number of students enrolled may exceed tens of thousands, it is impractical for a lecturer (or group of lecturers) to evaluate all students using conventional hand marking. Thus in order to be scalable, assessment must be automated. The state-of-the-art in automated assessment includes various methods and computerised tools including multiple choice questions, and intelligent marking techniques (involving complex semantic analysis). However, none of these completely cover the requirements needed for the implementation of an assessment system able to cope with very large populations of students and also able to guarantee the quality of evaluation required for higher education. The goal of this research is to propose, implement and evaluate a computer mediated social interaction system which can be applied to massive online learning communities. This must be a scalable system able to assess fairly and accurately student coursework and examinations. We call this approach “machine and socially intelligent peer assessment”. This paper describes our system and illustrates its application. Our approach combines the concepts of peer assessment and reputation systems to provide an independent computerised system which determines the degree and type of interaction between student peers based on a reputation score which emerges from the marking behaviour of each student and the interaction with other individuals of the community. A simulation experiment will be reported showing how reputation-based social structure can evolve in our peer marking system. A pilot experiment using a population of ninety 16-year old high school students in Colombia measured the marking accuracy of our system by comparing the statistical differences between the scores resulting from teacher marking (the ‘gold standard’), peer assessment using average scores, and our intelligent reputation-based peer assessment. This addresses the research question: to what extent does the proposed approach improve peer marking in terms of marking accuracy and fairness? We report the first results of this experiment, summarise the lessons learned, and describe further work

Learning through playing for or against each other? Promoting collaborative learning in digital game based learning

The process of learning through Game Based Learning (GBL) presents both positive aspects and challenges to be faced in order to support the achievement of learning goals and knowledge creation. This study aims to characterise game dynamics in the adoption of multi-player GBL. In particular, we examine the multi-player GBL dynamics may enhance collaborative learning through a relation of positive interdependence while at the same time maintaining a certain level of competition for ensuring multi-player GBL gameplay. The first section of the paper introduces collaborative GBL and describes the combination of intragroup dynamics of cooperation and positive interdependence and an intergroup dynamic of competition to maintain gameplay. The second part of the paper describes two multi-player GBL scenarios: the multi-player game with interpersonal competition and the multiplayer game with intergroup competition. For each scenario a case analysis of existing collaborative games is provided, which may help instructional and game designers when defining the collaborative GBL dynamics. Technological requirements and best practices in the use of collaborative GBL are described in the last sections

Predicting C-H/π\pi interactions with nonlocal density functional theory

We examine the performance of a recently developed nonlocal density functional in predicting a model noncovalent interaction, the weak bond between an aromatic $\pi$ system and an aliphatic C-H group. The new functional is a significant improvement over traditional density functionals, providing results which compare favorably to high-level quantum-chemistry techniques but at considerably lower computational cost. Interaction energies in several model C-H/$\pi$ systems are in generally good agreement with coupled-cluster calculations, though equilibrium distances are consistently overpredicted when using the revPBE functional for exchange. The new functional correctly predicts changes in energy upon addition of halogen substituents.Comment: 5 pages, 4 figure

Hypernetwork-based peer marking for scalable certificated mass education

In the context of the need for massive free education for the Complex Systems Society and the UNESCO Complex Systems Digital Campus, scalable methods are essential for assessing tens of thousands of students’ work for certification. Automated marking is a partial solution but has many drawbacks. Peer marking, where students mark each others’ assignments, is a scalable solution since every extra student is an extra marker. However there are concerns about the quality of peer marking, since some students may not be competent to mark the work of others. Some students are better than others and often the best students are well qualified to assess the work of their peers. To make peer marking high quality we are using new hypernetwork-based methods to extend previous methods to discover which students are good markers and which students are less good as a course progresses

Peer assessment in architecture education

The role of peer assessment in education has become of particular interest in recent years, mainly because of its potential benefits in improving student’s learning and benefits in time management by allowing teachers and tutors to use their time more efficiently to get the results of student’s assessments quicker. Peer assessment has also relevant in the context of distance learning and massive open online courses (MOOCs)

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