Wireless Network Access and Emotion Recognition of Online English Translation Teaching System from the Perspective of Artificial Intelligence

The research is aimed at verifying the application effect of the online automatic evaluation system in English translation teaching and at understanding the satisfaction of students with different feedback methods. The research uses three classes of human resource management majors in Xi’an Technological University as the research object and uses questionnaire survey and comparative experiment methods to compare and analyse the three feedback methods: teacher feedback, online automatic feedback, and teacher feedback combined with online automatic feedback. The research answers the following three questions: (1) will the three feedback methods affect students’ English translation performance? (2) Which of the three feedback methods will improve students’ English performance better? (3) What about students’ satisfaction with current feedback methods? The research results show that the significance value between the control group (CG) and the experimental group 1 (EG1) is 0.029, that between CG and the experimental group 2 (EG2) is 0.432, and that between EG1 and EG2 is 0.001. There are obvious differences in the posttest scores of the three groups of students. EG2 has the largest average posttest score, which is 9.8182; there is no obvious difference in posttest translation scores between CG and EG2. It indicates that “teacher feedback + online automatic feedback” and teacher feedback have the equivalent effect on improving students’ translation. The results of the questionnaire survey show that students have the highest degree of recognition of “teacher feedback + online automatic feedback.” The research is helpful for teachers to better understand the shortcomings in the translation teaching process, so that they can take effective measures against these problems in the follow-up teaching process to improve their teaching effect.

Using Dr. Scratch as a Formative Feedback Tool to Assess Computational Thinking

This study investigated if using Dr. Scratch as a formative feedback tool would accelerate students' Computational Thinking (CT). Forty-one 4th-6th grade students participated in a 1-hour/week Scratch workshop for nine weeks. We measured pre- and posttest results of the computational thinking test (CTt) between control (n = 18) and treatment groups (n = 23) using three methods: propensity score matching (treatment = .575; control = .607; p = .696), information maximum likelihood technique (treatment effect = -.09; p = .006), and multiple linear regression. Both groups demonstrated significantly increased posttest scores over their pretest (treatment = +8.31%; control = +5.43%), showing that learning to code can increase computational thinking over a 2-month period. In this chapter, we discuss the implications of using Dr. Scratch as a formative feedback tool the possibilities of further research on the use of automatic feedback tools in teaching elementary computational thinking.

L2 Czech Annotation for Automatic Feedback on Pronunciation

In this paper, we would like to provide a brief overview of the current state of pronunciation teaching in e-learning and demonstrate a new approach to building tools for automatic feedback concerning correct pronunciation based on the most frequent or typical errors in speech production made by non-native speakers. We will illustrate this in the process of designing annotation for a sound recognition tool to provide feedback on pronunciation. At the end of the paper, we will also present how we have tried to apply this annotation to the tool, what caveats we have found and what our plans are.

Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

IntelliRehabDS (IRDS)—A Dataset of Physical Rehabilitation Movements

In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals.