scholarly journals Optimal Hierarchical Learning Path Design With Reinforcement Learning

2020 ◽  
Vol 45 (1) ◽  
pp. 54-70
Author(s):  
Xiao Li ◽  
Hanchen Xu ◽  
Jinming Zhang ◽  
Hua-hua Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Model Free ◽  
Learning Path ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Efficient Learning ◽  
Skills Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

scholarly journals Recommending learning material in Intelligent Tutoring Systems

2016 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Jarosław Bernacki
Keyword(s):  
Learning Styles ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Psychological Research ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Tutoring Systems ◽  
Learning Path ◽  
E Learning

<p>Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.</p>

scholarly journals A method for learning scenario determination and modification in intelligent tutoring systems

2011 ◽  
Vol 21 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Adrianna Kozierkiewicz-Hetmańska ◽  
Ngoc Nguyen
Keyword(s):  
Learning Styles ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Educational Process ◽  
Learning System ◽  
Optimal Learning ◽  
Tutoring Systems ◽  
Learning Path ◽  
E Learning ◽  
Using Data

A method for learning scenario determination and modification in intelligent tutoring systemsComputers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. 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 path in each step of the educational process. The determination of a suitable learning path depends on the student's preferences, learning styles, personal features, interests and knowledge state. Therefore, the system has to collect information about the student, which is done during the registration process. A user is classified into a group of students who are similar to him/her. Using information about final successful scenarios of students who belong to the same class as the new student, the system determines an opening learning scenario. The opening learning scenario is the first learning scenario proposed to a student after registering in an intelligent e-learning system. After each lesson, the system tries to evaluate the student's knowledge. If the student has a problem with achieving an assumed score in a test, this means that the opening learning scenario is not adequate for this user. In our concept, for this case an intelligent e-learning system offers a modification of the opening learning scenario using data gathered during the functioning of the system and based on a Bayesian network. In this paper, an algorithm of scenario determination (named ADOLS) and a procedure for modifying the learning scenario AMLS with auxiliary definitions are presented. Preliminary results of an experiment conducted in a prototype of the described system are also described.

scholarly journals Profile reliability to improve recommendation in social-learning context

2018 ◽  
Vol 44 (2) ◽  
pp. 433-454 ◽  
Author(s):  
Corinne Amel Zayani ◽  
Leila Ghorbel ◽  
Ikram Amous ◽  
Manel Mezghanni ◽  
André Péninou ◽  
...  
Keyword(s):  
Social Network ◽  
Social Learning ◽  
Learning System ◽  
Learning Context ◽  
Content Type ◽  
Semantic Relationships ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Many Sources

Purpose Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

scholarly journals Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

scholarly journals Pengembangan Sistem Pembelajaran Berbasis Multimedia

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Suryanto Suryanto ◽  
Sunda Ariana ◽  
Syahril Rizal
Keyword(s):  
Information Technology ◽  
Positive Influence ◽  
Learning System ◽  
Learning Material ◽  
Literature Study ◽  
Multimedia Development ◽  
E Learning ◽  
The Media ◽  
Indispensable Tool ◽  
Audio Video

Currently the development of information technology has an important influence in the development of learning systems, one of which is technology in the multimedia field. Multimedia technology is an indispensable tool for the learning process. The use of text, images, audio, video, and animation in learning helps students to quickly understand learning material. Multimedia also provides opportunities for educators to develop learning methods so that they get maximum results. AMIK AKMI Baturaja is a computer college that has not fully utilized information technology in its learning, especially in the field of Multimedia. For this reason, researchers conducted this research for the development of learning at the Campus AMIK AKMI Baturaja. This study aims to produce a multimedia-based learning media at AMIK AKMI Baturaja which can be a driving force for student learning achievement. In this study using a research method of literature study, observation, questionnaires, and interviews. The media development method uses MDLC (Multimedia Development Life Cycle) which consists of six stages, namely concept (needs analysis), design (design), material collecting, assembly, testing, and distribution. . The result of this research is a multimedia-based learning system presented in the form of e-learning. This media is expected to have a positive influence in increasing student achievement and learning independence.

Automatic Organization and Generation of Presentation Slides for E-Learning

2012 ◽  
Vol 10 (3) ◽  
pp. 35-52 ◽  
Author(s):  
K. Sathiyamurthy ◽  
T. V. Geetha
Keyword(s):  
Distance Education ◽  
Language Processing ◽  
Learning System ◽  
Learning Material ◽  
Content Creation ◽  
Learning Content ◽  
E Learning ◽  
Processing Techniques ◽  
The Web ◽  
Extract Information

The effectiveness of an e-learning system for distance education to a large extent depends on the relevancy and presentation of learning content to the learner. The ability to gather documents on a particular topic from the web and adapt the contents of the document to suit the learner is an important task from the content creation perspective of e-learning. For the developer of e-learning material the provision to automatically extract, organize, and present content material would improve its effectiveness. This paper proposes to extract information from documents using language processing techniques and organizing the content into appropriate presentation slides for learning purposes using domain ontology and learning oriented pedagogy ontology.

Securing the Infrastructure for Service-Based E-Learning Environments

2011 ◽  
pp. 1-24
Author(s):  
Simon Schwingel ◽  
Gottfried Vossen ◽  
Peter Westerkamp
Keyword(s):  
Web Services ◽  
Learning Environment ◽  
Learning Environments ◽  
User Authentication ◽  
Learning Objects ◽  
Learning System ◽  
Learning Material ◽  
Single Sign On ◽  
User Data ◽  
E Learning

E-learning environments and their system functionalities resemble one another to a large extent. Recent standardization efforts in e-learning concentrate on the reuse of learning material only, but not on the reuse of application or system functionalities. The LearnServe system, under development at the University of Muenster, builds on the assumption that a typical learning system is a collection of activities or processes that interact with learners and suitably chosen content, the latter in the form of learning objects. This enables us to divide the main functionality of an e-learning system into a number of stand-alone applications or services. The realization of these applications based on the emerging technical paradigm of Web services then renders a wide reuse of functionality possible, thereby giving learners a higher flexibility of choosing content and functionalities to be included in their learning environment. In such a scenario, it must be possible to maintain user identity and data across service and server boundaries. This chapter presents an architecture for implementing user authentication and the manipulation of user data across several Web services. In particular, it demonstrates how to exploit the SPML and SAML standards so that cross-domain single sign-on can be offered to the users of a service-based learning environment. The chapter also discusses how this is being integrated into LearnServe.

scholarly journals The involvement of model-based but not model-free learning signals during observational reward learning in the absence of choice

2016 ◽  
Vol 115 (6) ◽  
pp. 3195-3203 ◽  
Author(s):  
Simon Dunne ◽  
Arun D'Souza ◽  
John P. O'Doherty
Keyword(s):  
Reinforcement Learning ◽  
Experiential Learning ◽  
Observational Learning ◽  
Ventral Striatum ◽  
Learning System ◽  
Model Based ◽  
Model Free ◽  
Human Participants ◽  
Open Question ◽  
Insight Into

A major open question is whether computational strategies thought to be used during experiential learning, specifically model-based and model-free reinforcement learning, also support observational learning. Furthermore, the question of how observational learning occurs when observers must learn about the value of options from observing outcomes in the absence of choice has not been addressed. In the present study we used a multi-armed bandit task that encouraged human participants to employ both experiential and observational learning while they underwent functional magnetic resonance imaging (fMRI). We found evidence for the presence of model-based learning signals during both observational and experiential learning in the intraparietal sulcus. However, unlike during experiential learning, model-free learning signals in the ventral striatum were not detectable during this form of observational learning. These results provide insight into the flexibility of the model-based learning system, implicating this system in learning during observation as well as from direct experience, and further suggest that the model-free reinforcement learning system may be less flexible with regard to its involvement in observational learning.

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