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We explore whether interactive navigational behaviours can be used as a reliable and effective source to measure the progress, achievement, and engagement of a learning process. To do this, we propose a data-driven methodology involving sequential pattern mining and thematic analysis of the low-level navigational interactions. We applied the method...
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Self-regulated learning (SRL) which has long been valued as an effective approach in English as a Foreign Language (EFL) is a broad term comprising both cognitive and affective components and concerning the notion of the learner-centred way of teaching. The allocation of resources towards the implementation of SRL is crucial in order to equip stude...
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... The classification results showed that organized and unorganized learning behaviors reflected different learning outcomes. Yu et al. [25] used the interaction data of students' online learning to generate learning sequences at intervals of every 5, 30, and 40 min, and then explore the relationship between learning patterns and learning outcomes. Çebi et al. [26] differentiate learning patterns into individual (micro-patterns) and collective (macro-patterns). ...
Improving learning outcomes is always one of the key objectives of learning analytics (LA) and educational data mining (EDM). In recent years, many Massive Open Online Courses (MOOC) have been deployed and making it easier to collect learners’ data for further analysis. Naturally, leveraging AI to process such kind of big data becomes one of the main research streams to support education. In this paper, we collected data and defined student learning patterns by leveraging online courses on Python programming and we then verified if their learning performance was influenced by different learning patterns and interventions. We designed the intervention process, explored the impact of final learning outcomes, and analyze Self-Regulated Learning (SRL) abilities. From the experimental results, we share the learning outcomes and the difference in SRL with detailed explanation based on different groups.
... This involves signing in to the learning platform, visiting the course page, examining the interface for the course activities, etc.; 2. Knowledge acquisition behavior (KAB): The behavior of online learners in acquiring knowledge directly, which occurs in the knowledge acquisition stage. It mainly includes participating in course activities, browsing course content resources, watching course videos, and visiting resource links; 3. Interactive learning behavior (ILB): It occurs during the interactive reflection stage and is one of the key learning behaviors in e-learning, which has been proven to have a positive effect on the continuity and effectiveness of e-learning [32]. It is manifested by participating in seminars, posting on forums, replying to forums, asking the teacher questions, etc.; 4. Learning consolidation behavior (LCB): Occurs during the learning consolidation stage and refers to the behavior of learners to reinforce their knowledge, mainly through post-lecture reflections and completion of post-lecture tests. ...
Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.
... Then we mined the data using the Java SPMF open source library in order to find sequential interaction patterns [11]. In particular, we used VMSP sequential pattern mining algorithm [12] to find the maximal sequential patterns since it eliminates overlapping sequences [41]. The analysis of the data was driven by two criteria: first, we aimed at maximising the minimum support (higher support indicates more users exhibiting the pattern) so the findings could be more generalisable. ...
... Wushu Achievement. In the process of constructing the relationship model between teaching quality and martial arts performance, first obtain the historical samples of students' martial arts performance estimation in each stage, give the observation matrix of students' martial arts performance, and then calculate the a priori state estimation value of students' martial arts performance, based on which the students' martial arts performance estimation model is established [13,14]. ...
The existing teaching quality evaluation methods cannot calculate the distance between the data points of teaching dataset and the center points of the large density grid, which leads to the poor classification of teaching data and the low accuracy of teaching quality evaluation. Therefore, a method of teaching quality evaluation of Wushu based on fuzzy clustering is proposed. In order to improve the comprehensiveness of teaching quality evaluation, the lost data of teaching resources were recovered. Based on this, the grid index of martial arts teaching data is established, and the relationship model between teaching quality and martial arts achievement is constructed. Based on the characteristics of Wushu teaching resources, the fuzzy clustering method is introduced to calculate the distance between each data point and the grid center with high data density. The experimental results show that the evaluation accuracy of the teaching quality is high, and the lost data can be accurately recovered. The evaluation efficiency, reliability, and stability of Wushu teaching quality are ideal.
... Online teaching requires more autonomy for learners [5]. In order to improve students' interest and autonomy in learning at home, teachers need to obtain timely feedback from students and master their learning dynamics [6]. Therefore, accurate analysis of academic conditions and teaching by learning are the key factors affecting the effect of online teaching. ...
The outbreak of the COVID-19 pneumonia epidemic has posed challenges to online teaching and cooperation in colleges and universities. In order to address these challenges, a smart learning system based on big data has been designed and developed by employing an path recommendation algorithm to implement the learning recommendation strategy. Several models are constructed to represent question domain, such as learning object model, question model and relationship model. We show how it can be applied to an actual prototype platform of smart learning system under the epidemic environment to demonstrate the usefulness of the proposed model.
Learners engaged in large-scale online learning often pose questions in which their peers or instructors can answer using various means of textual interaction topics. This paper assesses the effects of the text interaction strategies in online learning through the lens of the language expectancy theory at three levels: whether to respond to the questions, the identity of the respondents, and the textual interaction topics. Using 112,680 learning records of 610 courses from 71,948 learners crawled from the online learning programming platform iMOOC as the corpus, text mining is used to identify the interaction strategies. Using grounded theory, the textual interaction topics are divided into 2 groups (providing solutions, and encouragement & evaluation for the learners), and subdivided into 6 topic clusters (code writing, operation guidance, providing references, encouragement, normative interpretation , and opinion exchange). The responses are classified by text mining. The results of the econometric model suggest that responding to the questions online fosters learning and reduces the dropout rate. The online learner benefits more from peer learning than from the instructors. On the text interaction topics, the topic "provid-ing solutions" is more effective in reducing the learner's dropout rate than the topic "encouragement & evaluation". Further, code writing is more effective over providing references, encouragement, and normative interpretation. This study enriches our understanding of the interaction strategies between learners and instructors in iMOOC, and provides a reference for improving the online learning journey and retain learners.
