Combining Exploratory Learning With Structured Practice to Foster Conceptual and Procedural Fractions Knowledge

Robust domain knowledge consists of conceptual and procedural knowledge. The two types of knowledge develop together, but are fostered by different learning tasks. Exploratory tasks enable students to manipulate representations and discover the underlying concepts. Structured tasks let students practice problem-solving procedures step-by-step. Educational technology has mostly relied on providing only either task type, with a majority of learning environments focusing on structured tasks. We investigated in two quasi-experimental studies with 8-10 years old students from UK (N = 121) and 10-12 years old students from Germany (N = 151) whether a combination of both task types fosters robust knowledge more than structured tasks alone. Results confirmed this hypothesis and indicate that students learning with a combination of tasks gained more conceptual knowledge and equal procedural knowledge compared to students learning with structured tasks only. The results illustrate the efficacy of combining both task types for fostering robust fractions knowledge

Gaming in Middle Schools

Research has shown that when presented with an intelligent tutoring system, many students engage in gaming the system -- exploiting properties in the system rather than by learning the material and trying to use that knowledge to answer correctly [1]. We replicate a study that originally introduced a system which gives a gaming student supplementary exercises focused on exactly the material the student bypassed by gaming, and which also expresses negative emotion to gaming students through an animated agent [1]. The purpose was to determine whether if those engaged in gaming would learn less than those who did not. The system has shown to increase the learning in students who have the cognitive tutor over those who do not, and those who game learn less than those who do not

When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?

In this paper we investigate how student disengagement relates to two performance metrics in a spoken dialog computer tutoring corpus, both when disengagement is measured through manual annotation by a trained human judge, and also when disengagement is measured through automatic annotation by the system based on a machine learning model. First, we investigate whether manually labeled overall disengagement and six different disengagement types are predictive of learning and user satisfaction in the corpus. Our results show that although students’ percentage of overall disengaged turns negatively correlates both with the amount they learn and their user satisfaction, the individual types of disengagement correlate differently: some negatively correlate with learning and user satisfaction, while others don’t correlate with eithermetric at all. Moreover, these relationships change somewhat depending on student prerequisite knowledge level. Furthermore, using multiple disengagement types to predict learning improves predictive power. Overall, these manual label-based results suggest that although adapting to disengagement should improve both student learning and user satisfaction in computer tutoring, maximizing performance requires the system to detect and respond differently based on disengagement type. Next, we present an approach to automatically detecting and responding to user disengagement types based on their differing correlations with correctness. Investigation of ourmachine learningmodel of user disengagement shows that its automatic labels negatively correlate with both performance metrics in the same way as the manual labels. The similarity of the correlations across the manual and automatic labels suggests that the automatic labels are a reasonable substitute for the manual labels. Moreover, the significant negative correlations themselves suggest that redesigning ITSPOKE to automatically detect and respond to disengagement has the potential to remediate disengagement and thereby improve performance, even in the presence of noise introduced by the automatic detection process

The effectiveness of using intelligent tutoring systems to increase student achievement

Intelligent Tutoring Systems could be used to provide differentiated instruction. This review examines qualities of Intelligent Tutoring Systems and their impact on student achievement. Thirty peer-reviewed research studies published from 1997 to 2019 were selected for analysis. This review considers how intelligent tutoring systems compare with other methods of instruction, and how an intelligent tutoring system’s on-screen tutor impacts student achievement. Finally, this review considers methods of ITS personalization and how those methods impact student achievement. The reviewed research studies indicated that ITS was more effective than all forms of instruction except small group and individualized instruction. Additionally, on-screen agents in and personalization of Intelligent Tutoring Systems often have a positive impact on student learning. Recommendations for classroom implementation of intelligent tutoring systems and suggestions for future research are discusse

STUDY OF SPORTS TEACHERS STUDENTS' SKILLS FOR SELF-ASSESSMENT OF THEIR PROFESSIONAL QUALITIES

?n essential characteristic of the teacher’s profession is the constant process of self-improvement, which is impossible without existence of a specific personal position and criteria of the sport pedagogue. One of the trends for self-improvement is his personal and professional qualities. They are important because of their specific role – as a means of influence on the trained students in the educational process. Self-evaluation of the level of their growth is a regulator for the sport teachers conduct and activity because it determines the genuine orientation for the level of his qualities, the satisfaction or dissatisfaction

Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models

Intelligent Tutoring System Authoring Tools for Non-Programmers

An intelligent tutoring system (ITS) is a software application that tries to replicate the performance of a human tutor by supporting the theory of learning by doing . ITSs have been shown to improve the performance of a student in wide range of domains. Despite their benefits, ITSs have not seen widespread use due to the complexity involved in their development. Developing an ITS from scratch requires expertise in several fields including computer science, cognitive psychology and artificial intelligence. In order to decrease the skill threshold required to build ITSs, several authoring tools have been developed. In this thesis, I document several contributions to the field of intelligent tutoring in the form of extensions to an existing ITS authoring tool, research studies on authoring tool paradigms and the design of authoring tools for non-programmers in two complex domains - natural language processing and 3D game environments. The Extensible Problem Specific Tutor (xPST) is an authoring tool that helps rapidly develop model-tracing like tutors on existing interfaces such as webpages. xPST\u27s language was made more expressive with the introduction of new checktypes required for answer checking in problems belonging to domains such as geometry and statistics. A web-based authoring (WAT) tool was developed for the purpose of tutor management and deployment and to promote non-programmer authoring of ITSs. The WAT was used in a comparison study between two authoring tool paradigms - GUI based and text based, in two different problem domains - statistics and geometry. User-programming of natural language processing (NLP) in ITSs is not common with authoring toolkits. Existing NLP techniques do not offer sufficient power to non-programmers and the NLP is left to expert developers or machine learning algorithms. We attempted to address this challenge by developing a domain-independent authoring tool, ConceptGrid that is intended to help non-programmers develop ITSs that perform natural language processing. ConceptGrid has been integrated into xPST. When templates created using ConceptGrid were tested, they approached the accuracy of human instructors in scoring student responses. 3D game environments belong to another domain for which authoring tools are uncommon. Authoring game-based tutors is challenging due to the inherent domain complexity and dynamic nature of the environment. We attempt to address this challenge through the design of authoring tool that is intended to help non-programmers develop game-based ITSs