Adaptive Learning System Research Articles

What do critical reading strategies look like? Eye-tracking and lag sequential analysis reveal attention to data and reasoning when reading conflicting information

Critical reading is required for information literacy in the 21st century. However, how students read and process conflicting information regarding controversial issues is still not clear. Using eye-tracking technology, this exploratory study aimed to identify visual behavior patterns that characterized critical reading strategies when students processed conflicting information about socio-scientific issues, and to examine relationships among students' visual behavior, reading strategies, and reading task outcomes. Forty-eight undergraduate and graduate students participated and read conflicting texts about food-science issues. The conflicting texts were presented in the traditional Chinese language. Participants' eye movements were tracked during the entire reading process and their self-reported critical reading strategies, recall, and opinions on the issues were collected right after reading. Pearson's correlation analyses, lag sequential analyses, and content analyses were used to analyze the data. Results showed that participants with higher scores on self-reported critical reading strategies tended to pay more attention to reasoning information in conflicting reports and to include more judgments in their responses. Further, two significant visual behavior patterns were identified for critical reading: (1) after a pause for thinking or resting, critical readers' visual attention shifted directly back to reasoning information; and (2) critical readers' visual attention shifted between reasoning and data information. These two visual behavior patterns suggested that data inspection is important for critical reasoning about conflicting information. This study provided evidence for the applicability of eye-tracking technology in assessing critical reading strategies and relationships between eye-tracking data and self-reported critical reading strategies. Findings can contribute to the future design of adaptive learning systems and teaching of critical reading strategies.

The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning.

The outbreak of the two-year corona virus has made a great difference on existing methods of learning and instruction. Online education has become a crucial role to maintain non-stop learning after the post-epidemic period. The advanced technologies and growing popularity of network equipment have made it easy to deploy remote connections. However, teachers still face challenges when they actually implement distance courses. During the learning process, the quality of learning can be improved if the researchers consider multiple factors, including emotions, attitudes, engagement, cognition, neuroscientific and cultural psychology. After analyzing these factors, instructors can have better understanding of students’ mental building and cognitive understanding in their process of learning, and be familiar with the way of interaction with students and appropriately adjust their teaching. Therefore, the current study established a learning system that aimed to understand learners’ emotional signals during learning by applying the adaptive-feedback emotional computing technology. The purpose of the system was to allow learners to (1) self-examine their learning condition, (2) enhance their self-directed learning, (3) help learners who are in negative learning emotions or settings to lower anxieties, and (4) promote their learning attitudes and engagement. Result showed that the system with the adaptive-feedback emotional computing technology has significantly improved the learning effectiveness, lowered learning anxieties and increased students’ self-directed learning.

Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints.

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners’ progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.

Students’ Perceptions of ILS as a Learning-Style-Identification Tool in E-Learning Environments

This paper presents the evaluation of the Index of Learning Styles, an assessment tool of the Felder–Silverman learning model. A few studies have previously evaluated this tool, but as far as we know, none of them considered the learners’ opinion to achieve their goals. Considering that many studies suggest continuing with the Index of Learning Styles’ evaluation, an experimental study was conducted using Protus, developed as an adaptive learning system. Analysing the concurrent validity of the Index of Learning Styles, students’ learning preferences were acquired via two different tools: the Index of Learning Styles and the subjective questionnaire. Results suggest that the Index of Learning Styles is valid for defining learning style at the beginning of the learning process, resolving the cold-start problem. We found some differences between the results of the Index of Learning Styles and subjective assessment. By enhancing the Protus user interface with new functionality, which allows a free choice of the learning style during the learning process, we overcome the observed limitations of the Index of Learning Styles. This solution could be implemented in different personalised e-learning environments, regardless of the applied assessment tool, leading to a more reliable student model.

Continuity of Formal Power Series Products in Nonlinear Control Theory

Formal power series products appear in nonlinear control theory when systems modeled by Chen–Fliess series are interconnected to form new systems. In fields like adaptive control and learning systems, the coefficients of these formal power series are estimated sequentially with real-time data. The main goal is to prove the continuity and analyticity of such products with respect to several natural (locally convex) topologies on spaces of locally convergent formal power series in order to establish foundational properties behind these technologies. In addition, it is shown that a transformation group central to describing the output feedback connection is in fact an analytic Lie group in this setting with certain regularity properties.

DIGITAL TECHNOLOGIES AS A MEANS OF ADAPTIVE LEARNING FOR HIGHER EDUCATION INFORMATICS AND MATHEMATICS

Formulation of the problem. In the context of online / blended learning, the relevance of research on the use of digital technologies as a means of adaptive learning for higher education in computer science and mathematics is undeniable. Implementation of the principles of adaptive learning with the help of digital technologies provides significant opportunities for individualization and differentiation of the educational process by the cognitive characteristics and educational needs of each graduate. Materials and Methods. The study is based on the work of domestic and foreign scientists. Theoretical and practical methods are used. Using the methods of system analysis and theoretical generalization a study of the implementation of adaptive learning in distance learning at Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University. Results. It is substantiated that with the development of e-learning it became possible to effectively ensure the adaptability of the learning process of higher education in computer science and mathematics. Adaptation involves the analysis of individual characteristics and the initial level of training of students, the implementation of the process of interactive learning, evaluation of success criteria for the transition to the next level of education, and more. Methodical aspects of adaptive learning in the information educational system of a higher education institution are considered. Conclusions. The system of adaptive teaching of mathematics and computer science provides the optimal adaptation of the educational process to individual characteristics and personal preferences of students, promotes their cognitive activity, increases motivation to learn, provides monitoring of learning, and adjusts it to achieve planned individual results. The adaptive learning system has the potential to ensure the full involvement of students in the process of building their individual educational trajectory, developing their activities, improving the individualization of the educational process, and more.

Adaptive Body Schema Learning System Considering Additional Muscles for Musculoskeletal Humanoids

One of the important advantages of musculoskeletal humanoids is that the muscle arrangement can be easily changed and the number of muscles can be increased according to the situation. In this study, we describe an overall system of muscle addition for musculoskeletal humanoids and the adaptive body schema learning while taking into account the additional muscles. For hardware, we describe a modular body design that can be fitted with additional muscles, and for software, we describe a method that can learn the changes in body schema associated with additional muscles from a small amount of motion data. We apply our method to a simple 1-DOF tendon-driven robot simulation and the arm of the musculoskeletal humanoid Musashi, and show the effectiveness of muscle tension relaxation by adding muscles for a high-load task.

Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

With the advent of the era of big data and mobile Internet, the traditional teaching model of colleges and universities has been hit unprecedentedly. In the current big data and artificial intelligence network environment, new teaching concepts, characteristics, and model applications should be analyzed, and an education model based on artificial intelligence adaptive control and negative feedback adjustment mechanisms under the big data environment should be explored. The study of this model can provide practical experience and reference value for the application, popularization, and innovation of teaching information technology in the future. Combining the characteristics of big data games, this paper proposes a college student education model based on discrete dynamic model big data game. The scores of college students represent comprehensive information such as their qualifications to provide big data university education, the credit of historical records, and the degree of reliable data transmission. Based on the general model of the adaptive learning system, we have perfected the functions of each module in the overall structure of the domain-based special subject system and designed the overall workflow of the system. Computerized evaluation is introduced into the learner module, and an adaptive evaluation system structure based on cognitive diagnosis theory is designed to achieve a personalized topic selection model for different learners and enrich learners’ experience through more detailed and specialized evaluation results. The model lays the foundation for adaptive course path recommendation. This paper proposes a discrete real-time linear dynamic logic that can express discrete real-time characteristics and all regular characteristics. It can easily describe the behavior of some complex real-time systems. Experiments show that the discrete dynamic model big data game efficiency is improved by 35.71%, which can better optimize the education model of college students. In addition, it can be concluded that the research on the effectiveness of college student education in the era of big data not only determines the safety of the ideological field of colleges and universities but also affects the thinking orientation of college students.

Adaptive Learning System of Ontology using Semantic Web to Mining Data from Distributed Heterogeneous Environment

Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set.

An Adaptive Neuro-Fuzzy Inference System-Based Ubiquitous Learning System to Support Learners With Disabilities

Lack of standard learning infrastructures in secondary and tertiary institutions in most developing countries have made learning cumbersome for the disabled, as most available learning environments were designed to cater mainly for normal learners with little or no consideration for learners with disabilities, thereby resulting to poor academic performance among these affected groups. This paper implements an ANFIS (adaptive neuro-fuzzy inference system)-based ubiquitous learning middleware to support disabled learners by providing suitable learning content for them. The system evaluation showed the effectiveness of the developed ANFIS-based u-learning system in proffering solutions to some of the challenges faced by disabled learners as the system attained 94% correctness, 88% satisfaction, 78% validation, 78% system simplicity, 88% system feedback, and 94% efficiency for the learners.

Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models.

Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of Virtual Learning Environments (VLEs) instigating students to study from their homes. Every day a tremendous amount of data is generated when students interact with VLEs to perform different activities and access learning material. To make the generated data useful, it must be processed and managed by the proper machine learning (ML) algorithm. ML algorithms’ applications are many folds with Education Data Mining (EDM) and Learning Analytics (LA) as their major fields. ML algorithms are commonly used to process raw data to discover hidden patterns and construct a model to make future predictions, such as predicting students’ performance, dropouts, engagement, etc. However, in VLE, it is important to select the right and most applicable ML algorithm to give the best performance results. In this study, we aim to improve those ML and DL algorithms’ performance that give an inferior performance in terms of performance, accuracy, precision, recall, and F1 score. Several ML algorithms were applied on Open University Learning Analytics (OULA) dataset to reveal which one offers the best results in terms of performance, accuracy, precision, recall, and F1 score. Two popular ML algorithms called Decision Tree (DT) and Feed-Forward Neural Network (FFNN) provided unsatisfactory results. They were selected and experimented with various techniques such as grid search cross-validation, adaptive boosting, extreme gradient boosting, early stopping, feature engineering, and dropping inactive neurons to improve their performance scores. Moreover, we also determined the feature weights/importance in predicting the students’ study performance, leading to the design and development of the adaptive learning system. The ML techniques and the methods used in this research study can be used by instructors/administrators to optimize learning content and provide informed guidance to students, thus improving their learning experience and making it exciting and adaptive.

Importance of Teacher Planning and Training in the Operation of Private Middle and High School Adaptive Learning Systems

Importance of Teacher Planning and Training in the Operation of Private Middle and High School Adaptive Learning Systems

Evaluating the effectiveness of using the intelligent adaptive training system for design modules based on printed circuit boards

Objective. The article studies the problem of adequate assessment of the level of acquired knowledge and skills with design of modules based on printed circuit boards (PCB) using CAD PCB. The article considers the issue of assessing the effectiveness of the use of an intelligent adaptive training system, which is the fundamental criterion for the effectiveness of the training process for designing of modules based on PCB. This intelligent adaptive training system of design of modules based PCB enables learners to assess their own performances. The main purpose of the study is to substantiate the algorithm and mathematical models of the developed system for assessing the effectiveness of training using an intelligent adaptive system. Method. The parameters, algorithm and patterns of building intelligent adaptive learning systems for designing modules based on PCB of ship integrated control systems for future specialists are disclosed. Result. The target indicators of the effectiveness of training in the design modules based on PCB are highlighted. A fully functional algorithm for assessing the effectiveness of learning the design modules based on PCB in intelligent adaptive systems is determined by the step of technological operations to ensure the uniformity of the learning quality control. Conclusion. This system can be useful for teachers and specialists-studying the design modules based on PCB. This system can be used at the stage of assessing the achievable characteristics which can significantly speed up the learning process. The research methods are applied in training the design modules based on PCB to the personnel of the shipyard X52 in Vietnam. It is suitable not only for training shipyard personnel to work, for teaching various levels of training of personnel of any industrial enterprise, for example, in aircraft engineering, mechanical engineering or instrument making, and for teaching any CAD system in general.

Skills and fuzzy knowledge structures

In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning.

Learning Performance in Adaptive Learning Systems: A Case Study of Web Programming Learning Recommendations.

Students often face challenges while learning computer programming because programming languages’ logic and visual presentations differ from human thought processes. If the course content does not closely match learners’ skill level, the learner cannot follow the learning process, resulting in frustration, low learning motivation, or abandonment. This research proposes a web programming learning recommendation system to provide students with personalized guidance and step-by-step learning planning. The system contains front-end and back-end web development instructions. It can create personalized learning paths to help learners achieve a sense of accomplishment. The system can help learners build self-confidence and improve learning effectiveness. In study 1, the recommendation system was developed based on the personal data and feedback of 41 professional web design engineers. The system uses C4.5 decision tree methods to develop a programming learning recommendation model to provide appropriate learning recommendations and establish personalized learning paths. The test group included 13 beginner programmers. After 4 weeks’ programming instructions in front-end and back-end web development, the learners were interviewed to understand their preferences and learning effectiveness. The results show that the effectiveness of the recommendation system is acceptable. In study 2, online real-time feedback and adaptive instruction platform is developed, which is different from the past adaptive curriculums mainly using the Internet platform and only the submitted assignments to determine the newly recommended learning process for students. The study found that the students’ learning performance in the adaptive instruction group is better than those in the fixed instruction group.

A Cognitive Diagnosis Method in Adaptive Learning System Based on Preconceptions

One advantage of an adaptive learning system is the ability to personalize learning to the needs of individual users. Realizing this personalization requires first a precise diagnosis of individual users’ relevant attributes and characteristics and the provision of adaptability-enabling resources and pathways for feedback. In this paper, a preconcept system is constructed to diagnose users' cognitive status of specific learning content, including learning progress, specific preconcept viewpoint, preconcept source, and learning disability. The “Force and Movement” topic from junior high school physics is used as a case study to describe the method for constructing a preconception system. Based on the preconception system, a method and application process for diagnosing user cognition is introduced. This diagnosis method is used in three ways: firstly, as a diagnostic dimension for an adaptive learning system, improving the ability of highly-adaptive learning systems to support learning activities, such as through visualization of the cognition states of students; secondly, for an attribution analysis of preconceptions to provide a basis for adaptive learning organizations; and finally, for predicting the obstacles users may face in the learning process, in order to provide a basis for adaptive learning pathways.

Automatic Generation of Math Word Problems for Assessing Learner Skills in Adaptive Learning Systems

Automatic Generation of Math Word Problems for Assessing Learner Skills in Adaptive Learning Systems

Adaptive learning model for prin ted circuit boards (pcb) module designing in an intelligent learning system

The prospects for the adaptation usage in an intelligent learning system for designing modules based on printed circuit boards (PCB) are considered. Models of primary adaptation, training scenario, trained designer, adaptation of training materials, subject area, testing and practical tasks are created. The use of models in an adaptive learning system allows you to build a learning process aimed at consistent and unconditional learning of educational material for different levels of students. The practical significance of the work lies in the program for the implementation of the proposed models of adaptive control of the learning process according to the individual characteristics of the student in the development of an intelligent adaptive learning system. The results of the study have found practical application at the stages of the project development cycle at the Vietnamese shipyard X52 to increase the level of engineer competence in the design of modules based on PCB. Keywords adaptation, adaptive learning, intelligent learning system, module design, printed circuit board (PCB)

Fuzzy Tsukamoto Implementation to Detect Physiological Condition on IoT-Based e-Learning Users

Science and technology advancement drives humans to adapt to the digital world. IT development is proven to positively affect the education area through the concept of electronic learning (e-learning). This is especially true during the COVID-19 pandemic where traditional classrooms teaching was transferred to e-learning. This technological development demands individuals to adapt to the advancement. Despite its benefits, technological advancement may affect the physical condition of e-learning users. When the e-learning users fail to adjust, they might have physical condition problems that cause depression. Therefore, we propose an Internet of Things (IoT)-based system to detect the physiological conditions of e-learning users. By implementing Fuzzy Tsukamoto as artificial intelligence on IoT technology, we can identify the physiological condition of e-learning users such as relaxed, calm, anxious, and stressed conditions. Structurally, the proposed system consists of three stages: 1) Sensor data acquisition, 2) Physiological condition detection using Fuzzy Tsukamoto, 3) Display the output directly to the website. We evaluate the effectiveness of the proposed system in the task of detecting the physiological condition of the ten e-learning users. Based on experimental results, the proposed system presents 84.01% of accuracy. This result indicates that the proposed system is able to reliably detect physiological conditions on IoT-based e-learning users. By detecting psychological conditions, e-learning is expected to become an adaptive learning system so that it can adapt to the characteristics of each user.

Urnings: A New Method for Tracking Dynamically Changing Parameters in Paired Comparison Systems

AbstractWe introduce a new rating system for tracking the development of parameters based on a stream of observations that can be viewed as paired comparisons. Rating systems are applied in competitive games, adaptive learning systems and platforms for product and service reviews. We model each observation as an outcome of a game of chance that depends on the parameters of interest (e.g. the outcome of a chess game depends on the abilities of the two players). Determining the probabilities of the different game outcomes is conceptualized as an urn problem, where a rating is represented by a probability (i.e. proportion of balls in the urn). This setup allows for evaluating the standard errors of the ratings and performing statistical inferences about the development of, and relations between, parameters. Theoretical properties of the system in terms of the invariant distributions of the ratings and their convergence are derived. The properties of the rating system are illustrated with simulated examples and its potential for answering research questions is illustrated using data from competitive chess, a movie review system, and an adaptive learning system for math.