Understanding the role of individual learner in adaptive and personalized e-learning system

Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.

A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.

Representing Time Series Data in Intelligent Training Systems

Many of the most popular intelligent training systems, including driving and flight simulators, generate user time series data. This paper presents a comparison of representation options for two different student modeling problems: 1) early failure prediction and 2) classifying student activities. Data for this analysis was gathered from pilots executing simple tasks in a virtual reality flight simulator. We demonstrate that our proposed embedding which uses a combination of dynamic time warping (DTW) and multidimensional scaling (MDS) is valuable for both student modeling tasks. However, Euclidean distance + MDS was found to be a superior embedding for predicting student failure, since DTW can obscure important agility differences between successful and unsuccessful pilots.

Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.

FEATURES OF THE USE OF SIMULATION MODELING FOR MAKING MANAGEMENT DECISIONS ON THE DEVELOPMENT OF ENTREPRENEURIAL ACTIVITY TO EXAMPLE OF A TRADING COMPANY

Currently, for the effective development of any company, modern information technologies and information systems are used, including for making up-to-date decisions on the management of processes and personnel. In the context of the digitalization of the economy, special attention is paid to the business processes of interaction with clients of companies whose main goal is to increase the client base. In modern economic conditions, small business in Russia is developing steadily. Particularly relevant is the use of modern information technologies, which contribute to increasing the company's competitiveness, making new economic decisions, forming relevant strategies for the production and sale of products, goods, services, and building and retaining a customer base. To improve interaction with customers and the development of small businesses, the use of simulation modeling of queuing systems is of particular importance. In this study, used the GPSS World Student modeling environment, which allowed us to analyze the queuing system using the example of a grocery store. Computational experiments using the prepared simulation model have shown the direction of optimizing the number of points of payment for purchases when interacting with customers. Also, a preliminary forecast of the economic efficiency of the company with a different amount of used cash registers was carried out.

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.