Massive open online courses : an adaptive learning framework

Diverse student needs present a challenge in online education. Massive Open Online Courses (MOOCs) attract many diverse learners, so there is need to tailor the course instruction to meet the students’ individual needs. This paper investigates an adaptive MOOC system from a personalised learning perspective. Firstly, we review existing literature on adaptive online learning systems, bringing together findings on the relationship to both effective learning support and motivation to study. Secondly, we outline a proposed framework, which tailors the recommendation of instructional material using the learner’s profile. In this model, the system can present the user with a suggested learning path to meet appropriate learning objectives. As the student progresses, further recommendations can be made with appropriate resources to enhance and develop the learner’s understanding of the previous topics. Adaptation and personalised recommendation have been noted as providing the means for an online system to replicate, in part, the function of a human tutor. However, there are drawbacks both in the limitations of providing the best recommendations and in the danger of users having little control over their own learning. Allowing learners to manage their learning by setting objectives and developing paths has been associated with encouraging effective learning skills, increasing collaboration and enhancing learning. Our framework therefore supports users in creating their own paths, allowing them to make informed choices about appropriate resources based on their expression of current objectives and preferences. The framework will be evaluated by adapting an existing MOOC, allowing comparison of a variety of aspects including choice of learning path, learner satisfaction and effect on attainment and drop-out rate

Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system

AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment

This paper contains the design and development of an Adaptive Virtual Learning Environment (AdaptiveVLE) framework to assist educators of all disciplines with creating adaptive VLEs tailored to their needs and to contribute towards the creation of a more generic framework for adaptive systems. Fully online education is a major trend in education technology of our times. However, it has been criticised for its lack of personalisation and therefore not adequately addressing individual students’ needs. Adaptivity and intelligence are elements that could substantially improve the student experience and enhance the learning taking place. There are several attempts in academia and in industry to provide adaptive VLEs and therefore personalise educational provision. All these attempts require a multiple-domain (multi-disciplinary) approach from education professionals, software developers, data scientists to cover all aspects of the system. An integrated environment that can be used by all the multiple-domain users mentioned above and will allow for quick experimentation of different approaches is currently missing. Specifically, a transparent approach that will enable the educator to configure the data collected and the way it is processed without any knowledge of software development and/or data science algorithms implementation details is required. In our proposed work, we developed a new language/framework using MPS JetBrains Domain-Specific Language (DSL) development environment to address this problem. Our work consists of the following stages: data collection configuration by the educator, implementation of the adaptive VLE, data processing, adaptation of the learning path. These stages correspond to the adaptivity stages of all adaptive systems such as monitoring, processing and adaptation. The extension of our framework to include other application areas such as business analytics, health analytics, etc. so that it becomes a generic framework for adaptive systems as well as more usability testing for all applications will be part of our future work

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network

Topolor : a social personalized adaptive e-learning system

This paper briefly introduces Topolor, a social personalized adaptive e-learning system, which aims at improving fine-grained social interaction in the learning process in addition to applying classical adaptation based on user modeling. Here, we present the main features of Topolor and its preliminary evaluation that showed high system usability from a student’s perspective. The intention is to demonstrate Topolor hands-on at the conference

Incremental Adversarial Domain Adaptation for Continually Changing Environments

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.Comment: International Conference on Robotics and Automation 201