Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.
This study aims to create learning path navigation for target learners by discovering the correlation among micro-learning units. In this study, the learning path is defined as a sequence of learning units used to realize a learning goal, and a period used for realizing the learning goal is regarded as a learning cycle. Furthermore, the learning unit datasets are extracted according to the learning cycle. In order to discover the correlations of learning units, we proposed an algorithm named Bayesian Network Association Rule (BNAR), which is used to establish a dynamic learning path according to the learning history of reference learners group who achieved learning goals. Based on the successful learning history, the dynamic learning path navigation will help target learners to improve learning efficiency.
Augmented reality tools and applications have been shown to have powerfully impelled the development of the field of education. In this article, the authors designed and developed an augmented reality technology-based courseware “Starry Sky Exploration—Eight Planets in the Solar System” and explored how AR can bring an immersive learning experience to students and improve students' learning effectiveness. This article presents and evaluates AR courseware applicable for the geography curriculum in secondary schools in China. In this study, 36 students from Shanghai secondary vocational school were invited to participate in the experiment, the authors use reliability analysis, regression analysis and brainwave analysis to evaluate the effectiveness of the AR course. The authors found that students have higher learning satisfaction and behavioral willingness in AR-based experiential learning activities. It can be seen that AR helps to stimulate students' interest in learning.
In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.