Search Results (6)

Gamification and game-based learning provide the opportunity to acquire knowledge and skills on a given subject in a practical and interactive way. They are an innovative teaching methodology that could be used for competence acquisition in a variety of fields. This study focuses on two domains: technology (including robotics) and care. This study evaluates the usability of RobotCareMaker^®, a board game designed to teach care robotics, a branch of robotics oriented towards the study of human care. RobotCareMaker^® consists of 106 elements. The playing cards are the engine of the game and the element of interaction between players. A convenience sample was selected. Usability was evaluated by the System Usability Scale (SUS) questionnaire modified for the game, and three questions about the game experience were used. Using a modified SUS questionnaire, 21 participants rated it with an excellent score of 80.36. Over 90% found the instructions clear and error-free. RobotCareMaker^® allows teachers, professionals, and nursing students to integrate curricular competencies in novel topics such as care robotics. The result suggests that RobotCareMaker^® enhances learning in assistive robotics, improving competencies in education and healthcare. Full article

Self-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of education modernization. Behavior data of online learning platforms are an important carrier to reflect the learners’ initiative to plan, monitor, and regulate their learning process. Self-regulated learning (SRL) is one of the important skills to achieve learning goals and is an essential means to ensure the quality of online learning. However, there are still great challenges in studying the types and sequential patterns of learners’ self-regulated learning behaviors in online environments. In addition, for higher education, the defects of the traditional education mode are increasingly prominent, and self-regulated learning (SRL) has become an inevitable trend. Based on Zimmerman’s self-regulation theory model, this paper first classifies learning groups using the hierarchical clustering method. Then, lag sequence analysis is used to explore the most significant differences in SRL behavior and its sequence patterns among different learning groups. Finally, the differences in academic achievement among different groups are discussed. The results are as follows: (1) The group with more average behavior frequency tends to solve online tasks actively, presenting a “cognitive oriented” sequential pattern, and this group has the best performance; (2) the group with more active behavior frequency tends to improve in the process of trial and error, showing a “reflective oriented” sequence pattern, and this group has better performance; (3) the group with the lowest behavior frequency tends to passively complete the learning task, showing a “negative regulated” sequence pattern, and this group has poor performance. From the aspects of stage and outcome of self-regulated learning, the behavior sequence and learning performance of online learning behavior mode are compared, and the learning path and learning performance of different learning modes are fully analyzed, which can provide reference for the improvement of online learning platform and teachers’ teaching intervention. Full article

Several attempts have been made to explore the factors influencing teacher emotions, most of which focus on external factors such as student behaviors and classroom teaching. However, research on the links between internal factors and teacher emotions is scant. Based on the control value theory, this article explored the influence of junior secondary mathematics teachers’ error orientations on their emotions, and how teachers’ error orientations and emotions were related to students’ mathematics learning strategies. A sample of 70 junior high school mathematics teachers and their students (N = 2453) in mainland China participated in this study. Confirmatory factor analysis and multilevel structural equation modeling were used to analyze the data. The results showed that teachers’ positive error orientation increased their positive emotions and reduced their negative emotions, whereas teachers’ negative error orientation increased their negative emotions and reduced their positive emotions. Regarding the effects of teacher emotions, teachers’ positive emotions increased students’ positive mathematics achievement emotions and reduced their negative emotions. Meanwhile, students’ negative mathematics achievement emotions significantly reduced their adoption of desirable mathematics learning strategies. The findings highlight the importance of teachers’ positive error orientation and positive emotion for students’ mathematics learning. Full article

An inherent requirement of teaching using online learning platforms is that the teacher must analyze student activity and performance in relation to course learning objectives. Therefore, all e-learning environments implement a module to collect such information. Nevertheless, these raw data must be processed to perform e-learning analysis and to help teachers arrive at relevant decisions for the teaching process. In this paper, UBUMonitor is presented, an open-source desktop application that downloads Moodle (Modular Object-Oriented Dynamic Learning Environment) platform data, so that student activity and performance can be monitored. The application organizes and summarizes these data in various customizable charts for visual analysis. The general features and uses of UBUMonitor are described, as are some approaches to e-teaching improvements, through real case studies. These include the analysis of accesses per e-learning object, statistical analysis of grading e-activities, detection of e-learning object configuration errors, checking of teacher activity, and comparisons between online and blended learning profiles. As an open-source application, UBUMonitor was institutionally adopted as an official tool and validated with several groups of teachers at the Teacher Training Institute of the University of Burgos. Full article

In-class teaching evaluation, which is utilized to assess the process and effect of both teachers’ teaching and students’ learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students’ concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers’ media usage and teachers’ type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers’ style, which performs better than the statistical modeling module with the accuracy of 0.69. Full article

The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy. Full article