An Evaluation of a Meaningful Discovery Learning Support System for Supporting E-book User in Pair Learning

In this paper, an experiment was conducted to study the learning performance when learning new knowledge in groups with an e-book system and a meaningful discovery learning support environment. The participants studied target new knowledge with an e-book in pairs; at first, all the knowledge points that appear in the e-book were displayed and learners in each pair were encouraged to actively create relations between the knowledge concepts together; after completing the task, they can compare their learner-generated relations with expert-generated relations. The learning perception of one hundred and forty-three participants are analyzed and discussed

Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems.Comment: Submitted to IEEE transaction on IT

Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

This paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method

Multiplayer Serious Games Supporting Programming Learning

Computational thinking (CT) is crucial in education for providing a multifaceted approach to problem-solving. However, challenges exist such as supporting teachers' knowledge of CT and students' desire to learn it, particularly for non-technical students. To combat these challenges, Computer Supported Collaborative Learning (CSCL) has been introduced in classrooms and implemented using a variety of technologies, including serious games, which have been adopted across several domains aiming to appeal to various demographics and skill levels. This research focuses on a Collaborative Multiplayer Serious Game (MSG) for CT skill training. The architecture is aimed at young students and is designed to aid in the learning of programming and the development of CT skills. The purpose of this research is to conduct an empirical study to assess the multiplayer game gameplay mechanics for collaborative CT learning. The proposed game leverages a card game structure and contains complex multi-team multi-player processes, allowing students to communicate and absorb sequential and conditional logics as well as graph routing in a 2D environment. A preliminary experiment was conducted with four fourth-graders and eight sixth-graders from a French school in Morocco who have varying levels of understanding of CT. Participants were split into three groups each with two teams and were required to complete a 16-question multiple-choice quiz before and after playing the same game to assess their initial structural programming logics and the effectiveness of the MSG. Questionnaires were collected along with an interview to gather feedback on their gaming experiences and the game’s role in teaching and learning. The results demonstrate that the proposed MSG had a favourable effect on the participants’ test scores as the scores of 4 of the teams increased and 1 remained the same. All students performed well on the sequential and conditional logics, which was significantly better than the achievement of the Bebras test of the graph routing. Furthermore, according to the participants, the game provides an appealing environment that allows players to immerse themselves in the game and the competitive aspect of the game adds to its appeal and helps develop teamwork, coordination, and communication skills

Longitudinal Development of Refractive Error in Children With Accommodative Esotropia: Onset, Amblyopia, and Anisometropia.

Purpose: We investigated longitudinal changes of refractive error in children with accommodative esotropia (ET) throughout the first 12 years of life, its dependence on age at onset of ET, and whether amblyopia or anisometropia are associated with defective emmetropization. Methods: Longitudinal refractive errors in children with accommodative ET were analyzed retrospectively. Eligibility criteria included: initial hyperopia ≥+4.00 diopters (D), initial cycloplegic refraction before 4 years, at least 3 visits, and at least one visit between 7 and 12 years. Children were classified as having infantile (N = 30; onset ≤12 months) or late-onset (N = 78; onset at 18–48 months) accommodative ET. Cycloplegic refractions culled from medical records were converted into spherical equivalent (SEQ). Results: Although the initial visit right eye SEQ was similar for the infantile and late-onset groups (+5.86 ± 1.28 and +5.67 ± 1.26 D, respectively), there were different developmental changes in refractive error. Neither group had a significant decrease in hyperopia before age 7 years, but after 7 years, the infantile group experienced a myopic shift of −0.43 D/y. The late-onset group did not experience a myopic shift at 7 to 12 years. Among amblyopic children, a slower myopic shift was observed for the amblyopic eye. Among anisometropic children, the more hyperopic eye experienced more myopic shift than the less hyperopic eye. Conclusions: Children with infantile accommodative ET experienced prolonged hyperopia followed by a myopic shift after 7 years of age, consistent with dissociation between infantile emmetropization and school age myopic shift. In contrast, children with late-onset accommodative ET had little myopic shift before or after 7 years