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In online learning, students’ learning data such as time and logs are commonly used to predict the student’s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a “prediction and alert” model for students’ learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students’ continuous online and offline learning more effectively.

Purpose This study aims to use eye-tracking technology to conduct an empirical study about online learning process analysis, thus aiming to understand the attentional preferences and learning paths in online learners. Design/methodology/approach With eye movement tracking and data analysing technology, the Tobii X120 eye-tracking instrument, Tobii studio and online learning platform are used to record and visualise data of eye moving and learning steps during the real task-based online learning processes of 14 online learners. According to Barbara A. Soloman’s learning style classification framework, these learners’ learning style was presented in four dimensions. Based on data of eye moving, leaning style and operation in online course, the correlation about learners’ preferences of learning content, online learning paths and learning style were analysed based on according data. Findings The paper provides empirical insights about how change is brought about during online learning. It is found that there is no significant difference in attention preference between the students with the difference on the learning style of visual-verbal, although each person has a different attention preference on the learning content. Research limitations/implications The limitation of this study is that only one common type of video learning process is studied. The learning process of various types of instructional videos in online learning will be done in future research. Practical implications In this study, eye-movement tracking technology is used to understand students’ learning path and learning preference in the online learning process, which is helpful to optimise the online learning process and improve the efficiency of online learning. Social implications This research findings have been approved by relevant experts and have won the first prize in the school-level competition of South China Normal University in China. Originality/value In this study, the technology of psychology (eye-tracking technology) is introduced into the study of real task-based online learning process in the subject of educational technology, realising the integration of multi-disciplinary research techniques and methods.

Digital technology plays a unique role in the cultivation of students’ creative thinking, which helps them solve poorly structured problems with effective and original solutions. This study applied collaborative learning in a digital-technology-supported artificial intelligence (AI) course and aimed to explore the impact of collaborative learning on fourth-grade students’ creative thinking. According to whether a leadership role was assigned by a teacher and a final consensus was built in the group, four patterns of collaborative learning were designed for comparison in order to determine which pattern was more effective for the promotion of students’ creative thinking. In total, 37 fourth-grade students taking part in the study were divided into four groups, and each group adapted one of four patterns of collaborative learning. The Torrance Creative Thinking Test (TTCT-Figure) was used to test the pre- and post-creative thinking of the four groups of students. A paired-sample t-test was used to analyze the pre- and post-tests of students’ creative thinking to verify whether all four patterns of collaborative learning could improve the students’ creative thinking. One-way ANOVA was used to analyze the post-test results of the four groups’ creative thinking to determine the differences in the creative thinking of the four groups of students. The results indicated that the patterns of collaborative learning used by G1, G3, and G4 were effective in improving students’ creative thinking, but the pattern for G2 was not. Moreover, there were significant differences in the cultivation of students’ creative thinking via AI courses among these four patterns of collaborative learning. The G4 students, who had an assigned leadership role and consensus building, showed the greatest improvement in creative thinking. In particular, without an assigned leadership role and consensus building, students’ flexibility of creative thinking would be improved to a greater extent. Teachers can adapt the findings of this study in order to consciously train team leaders in the collaborative learning process and guide them to reach a consensus to achieve the goal of fostering creative thinking in digital-technology-supported courses. To be specific, teachers should let students participate in group collaborative learning in a free way to cultivate their flexibility.

Background Gallbladder cancer (GBC) is the main pathological type of biliary tract cancers. Due to its aggressive nature, GBC is usually diagnosed at advanced stages with limited therapeutic options and poor outcome, especially after failure of chemotherapy. Case presentation Herein, we report a recurrent metastatic GBC patient with strong programmed death-ligand 1 (PD-L1) expression (≥50%) who obtained a significant response to radiotherapy combining nivolumab treatment. Conclusions To our knowledge this is the first case presenting significant nivolumab response in a Chinese GBC patient. This remarkable response was most likely associated with the strong PD-L1 expression, and indicated that PD-L1 expression could be considered as a biomarker for nivolumab treatment in metastatic gallbladder cancer. However, more studies are needed for validation.

Engagement is an important factor influencing the effectiveness and quality of online learning programs and how satisfied online learners are with the online learning experience. Therefore, when developing online learning programs, ways to increase engagement should be one of the top priorities. To determine the parameters that increase engagement of independent online learners, this paper selects three types of quantitative data by which to assess online learning engagement: video playing options, video lecture viewing time, and concurrent learning behaviors. This data, collected from real-time observation and analysis of authentic online learning of 14,000 adult learners, was used to run a real-time growth algorithm that determined a few key parameters that increase independent-learning engagement. A few primary results of this study are: 1) real-time, dynamically calculated data representing general or individual engagement parameters of independent learning can help both teachers and learners be aware of, recognize, and adjust learning status accurately and effectively; 2) the algorithm can identify optimal parameters for online learning by analyzing numerical values of engagement, rules, and characteristics of online learning that are difficult to be observed directly; and 3) it is necessary to dynamically analyze learners' engagement based on their learning processes and behaviors in response to different video lectures to know the effectiveness and feasibility of such materials and to support the design, production, and modification of optimal learning materials.