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AbstractUsing an integration of the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and the Self-Determination Theory (SDT). This study explored university students’ motivation to learn through the use of gamification. A quantitative approach was used to obtain data from 60 students who participated in a gamified course utilizing a closed-ended questionnaire. The students were chosen using a random sampling method. Structural equation model (SEM) was used to assess the collected quantitative data. It was revealed through the study that the satisfaction attribute of motivation influences students’ engagement in learning whereas attention, relevance, confidence and relatedness do not. This research added to the existing discussion on the use of ARCS and SDT to explain why students are motivated to learn when gamification is used in universities.

Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills. [ABSTRACT FROM AUTHOR]

This paper examines the inaugural year of a rebranded and restructured self-access learning center (SALC), launched at a provincial university in Japan towards the beginning of the COVID-19 pandemic, with a focus on implementing, promoting, and managing a tutoring system, both online and face-to-face, while facing the challenges and limitations imposed by the pandemic. Even though the pandemic has forced most traditional face-to-face classes and on-campus activities online, there is still little research concerning online self-access services in Japan, as also noted by Davies et al. (2020). Existing research on SALL in the pandemic tends to focus on well-established SALCs. This paper aims to fill this gap in the literature by also discussing the challenges of online tutoring and revealing the solutions to overcome those challenges over the course of a whole academic year in a new SALC. Finally, it hopes to provide an insight into the management process, together with the administrative challenges faced by the researcher, acting as the sole tutor and coordinator of the SALC.

This paper presents the most recent version of V-GRAFFER, a novel system that we have been developing for Visual GRoup AFFEct Recognition research. This version includes new algorithms and features, as well as a new application extension for using and evaluating the new features. Specifically, we present novel methods to collect facial samples from other e-lecture applications. We use screen captures of lectures, which we track and connect with samples during the duration of e-educational events. We also developed and evaluated three new algorithms for drawing conclusions on group concentration states. As V-GRAFFER required such complex functionalities to be combined together, many corresponding microservices have been developed. The current version of V-GRAFFER allows drawing real-time conclusions using the input samples collected from the use of any tutoring system, which in turn leads to real-time feedback and allows adjustment of the course material. [ABSTRACT FROM AUTHOR]

One of the problems faced by people with blindness is access to materials presented in graphical form. There are many alternative forms of providing such information, but they are very often ineffective or have certain limitations. The development of mobile devices and touch sensors enabled the development of new tools to support such people. This study presents a solution called an accessible tutoring platform, using audio-tactile graphics for people with blindness. We aimed to research the influence of the developed platform for the alternative presentation of graphics information on better memorizing, recognizing, and learning. Another goal of the research was to verify the effectiveness of the proposed method for the alternative presentation of audio-tactile graphics. The effectiveness of the proposed solution was verified quantitatively and qualitatively on two groups of blind students from primary and secondary schools with the use of a developed platform and prepared materials for learning mathematics. The obtained research results show that the proposed method of verifying students' knowledge and auto-selecting exercises with adapted audio description positively influences the improvement of learning effectiveness. [ABSTRACT FROM AUTHOR]

This study is a literature review with educational evaluation mediated by intelligent tutoring systems (ITS) as its central axis seeking to establish state of the art on implementations executed in the last 20 years and their impact on the evaluation process. The PRISMA methodology was applied for the literature review; the studies were included using the R software and bibliometric techniques with a general search equation that allowed access to all ITS production in Scopus. Subsequently, with the help of artificial intelligence, text mining was used to identify topics of interest in the scientific community, followed by further filtering. Finally, the selected full texts were analyzed using the NVivo software to extract emerging challenges in the field, obtaining 163 full texts for analysis. Among the main findings, the primary purpose of evaluation in ITS was summative, peer and self-evaluation did not have the same level of importance as hetero evaluation, and ITS focus was quantitative. All of this allowed us to conclude that the analyzed texts did not implement a holistic perspective and therefore evidenced the need to establish a framework for constructing an ITS using current technologies that integrate the mentioned variables.

Using grading and feedback systems in the context of learning and teaching programming is quite common. During the last 20 to 40 years research results on several hundred systems and approaches have been published. Existing papers may tell researchers what works well in terms of educational support and how to make a grading and feedback system stable, extensible, secure, or sustainable. However, finding a solid basis for such kind of research is hard due to the vast amount of publications from a very diverse community. Hardly any recent systematic review includes data from more than 100 systems (most include less than 30). Hence, the authors started an endeavor to build a corpus of all task-based grading and feedback systems for learning and teaching programming that deal with source code and have been published in recent years. The intention is to provide the community with a solid basis for their research. The corpus is also designed to be updated and extended by the community with future systems. This paper describes the process of building the corpus and presents some meta-analysis that shed light on the involved research communities. [ABSTRACT FROM AUTHOR]

Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills.

Recently, intelligent tutoring systems (ITSs) aid in assisting instructional procedures and help students traverse online learning resources adaptively. It has laid a strong foundation and has grown rapidly in the last several years. Web-based learning platforms make it easier for students from diverse backgrounds with varying requirements, interests, and traits to learn remotely and provide instant access to any subject area and learning method. An ITS is a computer-based system that imitates human tutors and its goal is to give students quick, personalized teaching or feedback usually without the need for human teacher intervention. The purpose of this study is to analyze the artificial intelligence (AI)-driven autonomous interactive English learning language tutoring system. The AI-powered approach utilized by Duolingo facilitates real-time feedback and adjusts to the rate of the student, including exercises that are specifically designed to enhance the English learning language. Through the system’s use, users gradually remember grammar and vocabulary by going over topics at suitable times. A total of 125 students participated in this study. This questionnaire was created to investigate important areas of students’ learning of the English language. Therefore, their feedback offers a more accurate depiction of their opinions toward learning English. Spell and grammar check in the essay are taken into account by a thorough scoring algorithm that is used to evaluate the user-centric system. The data was analyzed using SPSS software. The results indicate that by offering a scalable, user-centric system that adjusts to the demands of specific learners, Duolingo’s AI-based technology greatly improves the English language learning process. The AI-driven language tutoring systems are highlighted in this study, along with possible advancements in conversational AI and the integration of innovative learning technologies to improve the quality of learning. [ABSTRACT FROM AUTHOR]

Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation from historical student data, as well as research showing positive impacts on student learning. However, there is a lack of research exploring the possibility of extending data-driven methods to provide higher-level assistance. Therefore, we modified our data-driven Next-Step hint generator to provide Waypoints, hints that are a few steps ahead, representing problem-solving subgoals. We hypothesized that Waypoints would benefit students with high prior knowledge, and that Next-Step hints would most benefit students with lower prior knowledge. In this study, we investigated the influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course. We found that Next-Step hints were more beneficial for the majority of students in terms of time, efficiency, and accuracy on the posttest. However, higher totals of successfully used Waypoints were correlated with improvements in efficiency and time in the posttest. These results suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them. [ABSTRACT FROM AUTHOR]

This paper examines the inaugural year of a rebranded and restructured self-access learning center (SALC), launched at a provincial university in Japan towards the beginning of the COVID-19 pandemic, with a focus on implementing, promoting, and managing a tutoring system, both online and face-to-face, while facing the challenges and limitations imposed by the pandemic. Even though the pandemic has forced most traditional face-to-face classes and on-campus activities online, there is still little research concerning online self-access services in Japan, as also noted by Davies et al. (2020). Existing research on SALL in the pandemic tends to focus on well-established SALCs. This paper aims to fill this gap in the literature by also discussing the challenges of online tutoring and revealing the solutions to overcome those challenges over the course of a whole academic year in a new SALC. Finally, it hopes to provide an insight into the management process, together with the administrative challenges faced by the researcher, acting as the sole tutor and coordinator of the SALC. [ABSTRACT FROM AUTHOR]

When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, as evidenced by prior work and our own experiences in the classroom. To address this, we designed a Code Tracing (CT)-Tutor. We varied the level of assistance provided in the tutor, based on (1) the interface scaffolding available during code tracing, and (2) instructional order, operationalized by when examples were provided, either before or after the corresponding problem was solved. We collected data by having participants use the tutor to solve code tracing problems (N = 97) and analyzed both learning outcomes and process data obtained by extracting features of interest from the log files. We used a multi-layered approach for the analysis, including standard inferential statistics and unsupervised learning to cluster students by their behaviors in the tutor. The results show that the optimal level of assistance for code tracing falls in the middle of the assistance spectrum included in the tutor, but also that there are individual differences in terms of optimal assistance for subgroups of individuals. Based on these results, we outline opportunities for future work around personalizing instruction for code tracing. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the skill level of users. In this study, we aim to demonstrate the feasibility that AI can serve as an effective teaching aid to train individuals to develop optimal intensity modulated radiation therapy (IMRT) plans.Methods and Materials: The training program is composed of a host of training cases and a tutoring system that consists of a front-end visualization module powered by knowledge models and a scoring system. The current tutoring system includes a beam angle prediction model and a dose-volume histogram (DVH) prediction model. The scoring system consists of physician chosen criteria for clinical plan evaluation as well as specially designed criteria for learning guidance. The training program includes six lung/mediastinum IMRT patients: one benchmark case and five training cases. A plan for the benchmark case is completed by each trainee entirely independently pre- and post-training. Five training cases cover a wide spectrum of complexity from easy (2), intermediate (1) to hard (2). Five trainees completed the training program with the help of one trainer. Plans designed by the trainees were evaluated by both the scoring system and a radiation oncologist to quantify planning quality.Results: For the benchmark case, trainees scored an average of 21.6% of the total max points pre-training and improved to an average of 51.8% post-training. In comparison, the benchmark case's clinical plans score an average of 54.1% of the total max points. Two of the five trainees' post-training plans on the benchmark case were rated as comparable to the clinically delivered plans by the physician and all five were noticeably improved by the physician's standards. The total training time for each trainee ranged between 9 and 12 h.Conclusion: This first attempt at a knowledge model based training program brought unexperienced planners to a level close to experienced planners in fewer than 2 days. The proposed tutoring system can serve as an important component in an AI ecosystem that will enable clinical practitioners to effectively and confidently use KBP.

Introduction: an Important task of national pedagogy is related to the understanding and analysis of the experience of educational systems of other countries that have a centuries-old pedagogical. The aim is to evaluate and use the most successful and effective developments of domestic pedagogy.The experience of the UK is leading in relation to the development of individualization of education. This experience is elite, as the traditional tutor training system has been developed and implemented for decades, it has proved its effectiveness in the course of interaction with many national pedagogical systems.Materials and methods: theoretical methods are fundamental for this study: the method of analogy of the concepts of mentoring, tutoring, support, maintenance; method of comparison, consisting in the consideration of the practice of tutoring from the Middle Ages to the present day and the experience of tutoring at different educational levels in the UK method of analyzing the experience of tutoring activities in educational practice in the UK and the experience of using the tutoring system in our days.Results: One of the most important elements of the educational system of great Britain is a method of tutor student interaction and student in teaching and education, the occurrence of which relates to the period of the middle Ages. Technology tutor support involves thoughtful and well-organized psychological and educational support, namely assistance to students in identifying and solving abilities to reconcile their desires with real possibilities in identifying opportunities for growth and advancement in personal development, in orientation in a rapidly changing world. During the entire period of study, the student needs guidance and support, including effective response to emerging issues, ensuring the implementation of needs as they arise.In the analysis of the tutor system of education as a means of improving the quality of education, we can not agree with the conclusion of many scientists, based on statistics from leading universities in England, that the improvement of academic achievements of students of both higher education institutions and schools is directly related to the tutor system of education.Discussion and conclusions: foreign experience, in our case English education, containing a constructive practice of improving the quality of the educational process, becomes relevant for modern Russian education. Studying and analyzing the experience of the organization of tutor support in England, we can determine the incentive for a wider awareness of pedagogical reality, more clearly see the problems and solutions and identify areas for further development of Russian education in this area.