36,050 research outputs found
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
A case-based system for lesson plan construction
Planning for teaching imposes a significant burden on teachers, as teachers need to prepare different lesson plans for different classes according to various constraints. Statistical evidence shows that lesson planning in the Malaysian context
is done in isolation and lesson plan sharing is limited. The purpose of this thesis is to
investigate whether a case-based system can reduce the time teachers spend on
constructing lesson plans. A case-based system was designed SmartLP. In this
system, a case consists of a problem description and solution pair and an attributevalue
representation for the case is used. SmartLP is a synthesis type of CBR
system which attempts to create a new solution by combining parts of previous
solutions in the adaptation.
Five activities in the CBR cycle retrieve, reuse, revise, review and retain
are created via three types of design: application, architectural and user interface.
The inputs are the requirements and constraints of the curriculum and the student
facilities available, and the output is the solution, i.e. appropriate elements of a
lesson plan. The retrieval module consists of five types of search advanced search,
hierarchical, Boolean, basic and browsing. Solving a problem in this system involves
obtaining a problem description, measuring the similarity of the current problem to
previous problems stored in a database, retrieving one or more similar cases and
attempting to reuse the solution of the retrieved cases, possibly after adaptation.
Case adaptation for multiple lesson plans helps teachers to customise the retrieved
plan to suit their constraints. This is followed by case revision, which allows users to
access and revise their constructed lesson plans in the system. Validation
mechanisms, through case verification, ensure that the retained cases are of quality.
A formative study was conducted to investigate the effects of SmartLP on
performance. The study revealed that all the lesson plans constructed with SmartLP
assistance took significantly less time than the control lesson plans constructed
without SmartLP assistance, although they might have access to computers and
other tools. No significant difference in writing quality, measured by a scoring system,
was noticed for the control group, who constructed lesson plans on the same tasks
without receiving any assistance. The limitations of SmartLP are indicated and the
focus of further research is proposed.
Keywords: Case-based system, CBR approach, knowledge acquisition, knowledge
representation, case representation, evaluation, lesson planning
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Optimising multi-disciplinary contributions for the smart clothing development process
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A Literature Review on Intelligent Services Applied to Distance Learning
Distance learning has assumed a relevant role in the educational scenario. The use of
Virtual Learning Environments contributes to obtaining a substantial amount of educational data.
In this sense, the analyzed data generate knowledge used by institutions to assist managers and
professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide
variety of intelligent services for assisting in the learning process. This article presents a literature
review in order to identify the intelligent services applied in distance learning. The research covers
the period from January 2010 to May 2021. The initial search found 1316 articles, among which
51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning
systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems
or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the
principal services offered are recommendation systems and learning systems. In these services, the
analysis of student profiles stands out to identify patterns of behavior, detect low performance, and
identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio
Design of a recommender system for web based learning
The design of recommender systems is an ongoing research area where several researchers have devised means of incorporating intelligence in web content systems to be able to provide recommendations to learners on the basis of their learning preferences i.e. based on their learning profiles. The paper discusses the design of such a system based mapped to a content ontology and learner profiles created in the system
Collaboration Versus Cheating
We outline how we detected programming plagiarism in an introductory online
course for a master's of science in computer science program, how we achieved a
statistically significant reduction in programming plagiarism by combining a
clear explanation of university and class policy on academic honesty reinforced
with a short but formal assessment, and how we evaluated plagiarism rates
before SIGand after implementing our policy and assessment.Comment: 7 pages, 1 figure, 5 tables, SIGCSE 201
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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