1,343 research outputs found
Supporting teachers in collaborative student modeling: a framework and an implementation
Collaborative student modeling in adaptive learning environments allows the learners
to inspect and modify their own student models. It is often considered as a
collaboration between students and the system to promote learners’ reflection and
to collaboratively assess the course. When adaptive learning environments are used
in the classroom, teachers act as a guide through the learning process. Thus, they
need to monitor students’ interactions in order to understand and evaluate their
activities. Although, the knowledge gained through this monitorization can be extremely
useful to student modeling, collaboration between teachers and the system
to achieve this goal has not been considered in the literature. In this paper we
present a framework to support teachers in this task. In order to prove the usefulness
of this framework we have implemented and evaluated it in an adaptive
web-based educational system called PDinamet.Postprint (author's final draft
A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning
The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones.
The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning.
One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance.
The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design.
We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future
Enriching e-learning metadata through digital library usage analysis
Purpose: In this paper we propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of
the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries.
Methodology: We present experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment looks into raw server log data generated from the interactions of the students
with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects
and the learning process.
Findings: Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish elationships
between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means.
Research limitations: During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also search for
information outside the virtual campus. Not all of these usage data are considered in the model we propose. Further research needs to be done in order to get a complete view of the information search behavior of students for improving the users’ profile and creating better personalized services.
Practical implications: In this paper we suggest how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community.
Originality: Ever since the beginnings of libraries, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source
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
Applying Recommender Systems and Adaptive Hypermedia for e-Learning Personalizatio
Learners learn differently because they are different -- and they grow more distinctive as they mature. Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper we present our design and implementation of an adaptive and intelligent web-based programming tutoring system -- Protus, which applies recommendation and adaptive hypermedia techniques. This system aims at automatically guiding the learner's activities and recommend relevant links and actions to him/her during the learning process. Experiments on real data sets show the suitability of using both recommendation and hypermedia techniques in order to suggest online learning activities to learners based on their preferences, knowledge and the opinions of the users with similar characteristics
Latent Dirichlet Allocation (LDA) for improving the topic modeling of the official bulletin of the spanish state (BOE)
Since Internet was born most people can access fully free to a lot sources of information. Every day a lot of web pages are created and new content is uploaded and shared. Never in the history the humans has been more informed but also uninformed due the huge amount of information that can be access. When we are looking for something in any search engine the results are too many for reading and filtering one by one. Recommended Systems (RS) was created to help us to discriminate and filter these information according to ours preferences. This contribution analyses the RS of the official agency of publications in Spain (BOE), which is known as "Mi BOE'. The way this RS works was analysed, and all the meta-data of the published documents were analysed in order to know the coverage of the system. The results of our analysis show that more than 89% of the documents cannot be recommended, because they are not well described at the documentary level, some of their key meta-data are empty. So, this contribution proposes a method to label documents automatically based on Latent Dirichlet Allocation (LDA). The results are that using this approach the system could recommend (at a theoretical point of view) more than twice of documents that it now does, 11% vs 23% after applied this approach
A review on massive e-learning (MOOC) design, delivery and assessment
MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft
Affective e-learning approaches, technology and implementation model: a systematic review
A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling
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