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.

Benefits of Adaptive Learning Transfer From Typing-Based Learning to Speech-Based Learning

Memorising vocabulary is an important aspect of formal foreign-language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in word learning where spoken instead of typed input is used. Here we present a framework for speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes. We extend earlier findings demonstrating that a response-time based adaptive learning approach outperforms an accuracy-based, Leitner flashcard approach in learning efficiency (demonstrated by higher average accuracy and lower response times after a learning session). In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. Our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner’s pronunciations. We discuss the implications for our approach for the development of educationally relevant, adaptive speech-based learning applications.

Providing equitable education through personalised adaptive learning

Educators around the globe are striving to promote equity in their classrooms by adopting adaptive learning systems to provide customised learning resources and tools to help the learners achieve mastery at their own pace. This personalised approach goes a long way in closing the divide between students' socioeconomic status and special needs. Over the years, the advancement in technology offers more sophisticated adaptive technologies. It aggregates data such as students' prior knowledge and academic performances to predict and better adapt the learning paths. This paper presents the evaluation of adaptive technologies for personalised learning and the vision of a Personalised Adaptive Learning and Assessment (PALAS) system for Higher Education. This vision could be an imperative piece supporting Singapore's ‘National AI Strategy’, set to focus on personalised education through adaptive learning and assessment.

Adaptive Learning Systems and Interference in Causal Inference

Adaptive learning systems can be susceptible to between-subject cross-condition interference by design. This interference has important implications for the implementation and evaluation of A/B tests in such systems, as it obstructs causal inference and hurts external validity. We illustrate the problem in an Elo based adaptive learning system, discuss sources and degrees of interference, and provide solutions, using an example in the study of dropout.

Benefits of Adaptive Learning Transfer from Typing-Based Learning to Speech-Based Learning

Memorising vocabulary is an important aspect of formal foreign-language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes, and we extend earlier findings demonstrating that a response-time based adaptive learning system outperforms an accuracy-based, Leitner flashcard learning algorithm. In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. Our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner's pronunciations. The development of adaptive, speech-based learning applications is important for two reasons. First, by focusing on speech, the model can be applied for individuals whose typing skills are insufficient---as is demonstrated by the successful application of the model in an elderly participant population. Second, speech-based learning models are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.

HE METHOD OF COMBINED ONLINE ASSESSMENT ENGAGEMENT IN ADAPTIVE LEARNING SYSTEMS

Modern trends in the development of educational software are associated with adaptive learning systems that can personalize the learning process. One of the key quality indicators of any learning system is user engagement in learning process. The known methods for assessing engagement of users of computer systems are based on collecting of various data on the user’s behavior, his emotional and neurophysiological state, etc., and interpreting it in the context of involvement. Confidence in the results of assessments based on interpretations may be insufficient to make decisions based on these results, which is of fundamental importance in case of using in adaptive learning systems. And besides not all the methods can be used online, which is a prerequisite for application in adaptive learning systems. A method for online assessing engagement has been developed, based on a combination of oculography, emotion analysis and web analytics methods, which is applicable for adapting the learning process in adaptive learning systems. The joint analysis of the physiological state and behavior (actions) of the user allows to take into account various aspects of the possible manifestation of the user engagement, and increase the confidence in the results of the assessment of engagement. Quantitative engagement indicators based on metrics applicable to online engagement assessment are suggested. Due to ambiguity of possible interpretations of quantitative indicators of involvement, a generalized assessment of engagement is determined using a fuzzy inference mechanism. To assess engagement a linguistic variable is used and a method for assessing user engagement is based on fuzzy rules. The proposed method is implemented in a web application that can be used to assess involvement online in adaptive learning systems. To set the linguistic variable for assessing engagement, an experiment will be conducted with the participation of users of learning systems and external experts. The values of the linguistic variable will be defined in such a way that the result of assessing involvement, obtained using the developed method, does not contradict the results of expert assessments.

FUZZY LOGIC IN CREATING ADAPTIVE INTELLIGENT LEARNING

the article discusses the relevance and effectiveness of individualization of online learning using information and Internet technologies, the features of a multi-agent approach using fuzzy logic, an analysis of intelligent adaptive learning systems with different types is given, their models, advantages and disadvantages are also given.