Multi-scenario Modelling of Learning

Springer eBooks, 2020

To improve the learning process, the evolution of learner's characteristics (cognitive, affective, prior knowledge, workflow, organization, ...) must be taken into account during the personalization or adaptation. This requires generating several scenarios (a description of activities, their order and links in the learning sequence as well as the expected outcome for the learner) adapted to the identified profiles. We propose a model which aims at improving learners' learning processes by giving them control over two key aspects: (1) the steps of the learning scenario to be followed: after each learning goal is completed, the learner chooses the next one among several possible ones (in terms of their current knowledge) while respecting pedagogical constraints (time and quality of the solutions produced according to satisfaction thresholds); (2) the assessment mode: the learner chooses a mode corresponding to their own goals in terms of mastery, while respecting the minimum thresholds set by the teacher. We assess our approach with learners in terms of (a) adequacy of the model with learners' expectations and (b) usability of the system through self-report questionnaires and an analysis of the data collected over 16 learners who used an implementation of our system on the LMS (Learning Management System) in the context of a real computer course. The results reveal that our model is mostly well-received, that various scenarios are indeed followed by the learners and that the 3 assessment modes have been used by learners.

Journal of Universal Computer Science, 2008

One of the key problems in developing standard based adaptive courses is the complexity involved in the design phase, especially when establishing the hooks for the dynamic modelling to be performed at runtime. This is particularly critical when the courses are based on adaptation-oriented learning scenarios, where the full eLearning cycle (design, publication, use and auditing) is considered. Based on the problems we experienced in developing such scenarios with a reusable, platform independent, objective-based approach in the aLFanet project we have established an alternative framework in the ADAPTAPlan project, which focuses on dynamically generating learning design templates with the support of user modelling, planning and machine learning techniques. In particular, in this paper we describe the problems we are tackling and how we are relaxing the design work by automatically building the IMS learning design of the course from a simplified set of data required from the course authors.

The 8th International …, 1999

2018

Computers have been employed in education for years. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning scenario in each step of the educational process. The determination of a suitable learning scenario depends on the student’s skills, ability to answer on predefined questions, learning styles, personal features, interests and knowledge state. The importance of acquisition of the final educational result is considered to be the main disadvantage in the classic test system where the learner chooses the correct answer from the suggested set of answers using predefine question selection algorithm; this does not motivate a person to define the answer themselves or to create a logical chain of problem solutions. The integration of the intellectual processes into training systems will prevent the drawbacks of the existing knowledge assessment systems and will make it possi...

2021

Through this work, based on research works concerning e-learning in particular works dealing with e_learning project management systems, adaptive hypermedia systems and decision-making systems on the one hand. Work on the impact of project-based learning on self-regulation in teaching and the design of pedagogical scenarios for classical education and in particular for modular online education on the other hand. Our work concerns the design and scripting of a modular online education by proposing different possible scenarios according to the nature of the learning or assessment activities linked to a learning situation without taking into account the nature of the discipline and the nature of the concept to be addressed.

Among the main issues of future technology-enhanced learning systems, we can mention the following ones: the ability to reuse learning resources (learning objects, tools and services) from large repositories, to take into account the context and to allow dynamic adaptation to different learners based on substantial advances in pedagogical theories and knowledge models. In our framework, the goal of scenarios is to describe the learning and tutoring activities to acquire some knowledge domain (for instance physics) and knowhow to solve a particular problem. The main contribution of this paper is an adaptive and context-aware model of scenario based on a didactical theory and closely related to a domain model, a learner model, a context model. These models are acquired from: i) the know-how and real practices of teachers in a problem-based learning approach in a particular framework: an institution IUFM, different categories of probationary teachers, a course about "the air as gas in its static and dynamic aspects: properties, theory and applications"; ii) the theory in didactic anthropology of knowledge of Chevallard [1]; iii) a hierarchical task model.

2015

Assistance systems require a growing interest from a large community of researchers especially in the context of online learning, their main purpose is to provide a proactive or reactive support to accomplish a learning activity. The diversity of roles and skills required in tutoring (perception of behaviour, interaction analysis, increasing motivation and regulation rhythm, etc.) makes the task of tutor arduous and difficult to manage. Thus, the assistance of tutors is the main focus of several research offering assistance of answers in the form of learning situations models and support tools for the various tasks that the tutor must ensure to his learners. However, elementary and repetitive tasks of tutor are rarely taken into account specifically for their proactive character. Therefore, we propose to support learning scenario of learner by an assistance scenario suitable for his learning style, on the basis of a set of pre-defined assistance objects. The assistance of tutors allows then, on the one hand, to advise them on interventions that best respond to the need of learner, and on the other hand, to trigger an automatic assistance according to specific conditions, discharging the tutor automatable repetitive and arduous tasks. Our contribution aims to relieve the tutor in his simple and monotonous tasks thanks to prediction method based on the Model of Assistance Object (MAO). The model aims to facilitate to tutor the definition of assistance objects more precisely. These assistance objects are used then by our prediction method to predict those most suitable depending on the learner's context, his learning style, and the feedback on the impact of objects of assistance which were previously presented.

Over recent years, Model-Driven-Engineering has attracted growing interest as much as a research domain as an industrial process that can be applied to various educational domains. This article aims to discuss and propose such an application for learning-scenario-centered instructional design processes. Our proposition is based on a 3-domain categorization for learning scenarios. We also discuss and explain why we think Domain-Specific Modeling techniques are the future new trend in order to support the emergence of communities of practices for scenario-based instructional design. The originality resides in the support we propose to help communities of practitioners in building specific Visual Instructional Design Languages with dedicated editors instead of providing them with yet another language or editor.

In order to effectively exploit the wealth of content in Learning Object Repositories several issues should be addressed including the "closed corpus" problem as identified in the field of Adaptive Hypermedia as well as the "one size fits all" problem. Both are related to personalization. The creation of personalized learning experiences is considered as a necessity to cope with the overwhelming amount of available learning material. This paper presents a personalization framework that allows for the automatic creation of pedagogically-sound learning experiences taking into account the variety of the Learners and their individual needs. This framework defines a model for the representation of abstract training scenarios (Learning Designs) encoded in an instructional ontology. This ontology clearly separates pedagogy from content allowing this way the construction of real personalized learning experiences where learning objects are bound to the learning scenario at run-time taking into account information encoded in Learner Profiles.

International Journal of Recent Technology and Engineering (IJRTE), 2019

Transition of traditional online learning towards the Smart learning environments requires adaptation of the smart features. Personalized assistance is one of the most required characteristic. Learning plans are important building blocks for any teaching learning paradigms. Often an instructor makes a learning plan keeping stakeholders into consideration. However in an online learning environment the stakeholders are not able to adapt to these static plans as there is no personalization involved. As a result there is a considerable increase in dropout rates. Hence there is a need for adaptive learning plans which aims for dynamic adjustment of schedules/learning plans that may help in successful completion of course. This paper presents an approach for dynamic learning plan generator and also proposes a revised Learning plan template to achieve personalized assistance in Smart learning environments. The responses of stakeholders on traditional learning plans and that of individualiz...