Search

PurposeThe paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.Design/methodology/approachGuided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.FindingsThe study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.Originality/valueFor theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

PurposeCritical thinking is considered important in psychological science because it enables students to make effective decisions and optimizes their performance. Aiming at the challenges and issues of understanding the student's critical thinking, the objective of this study is to analyze online discussion data through an advanced multi-feature fusion modeling (MFFM) approach for automatically and accurately understanding the student's critical thinking levels.Design/methodology/approachAn advanced MFFM approach is proposed in this study. Specifically, with considering the time-series characteristic and the high correlations between adjacent words in discussion contents, the long short-term memory–convolutional neural network (LSTM-CNN) architecture is proposed to extract deep semantic features, and then these semantic features are combined with linguistic and psychological knowledge generated by the LIWC2015 tool as the inputs of full-connected layers to automatically and accurately predict students' critical thinking levels that are hidden in online discussion data.FindingsA series of experiments with 94 students' 7,691 posts were conducted to verify the effectiveness of the proposed approach. The experimental results show that the proposed MFFM approach that combines two types of textual features outperforms baseline methods, and the semantic-based padding can further improve the prediction performance of MFFM. It can achieve 0.8205 overall accuracy and 0.6172 F1 score for the “high” category on the validation dataset. Furthermore, it is found that the semantic features extracted by LSTM-CNN are more powerful for identifying self-introduction or off-topic discussions, while the linguistic, as well as psychological features, can better distinguish the discussion posts with the highest critical thinking level.Originality/valueWith the support of the proposed MFFM approach, online teachers can conveniently and effectively understand the interaction quality of online discussions, which can support instructional decision-making to better promote the student's knowledge construction process and improve learning performance.

Purpose This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed. Design/methodology/approach The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method. Findings Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students. Research limitations/implications The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation. Originality/value This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions.

Distance education programs have become the preferred option for most higher education institutions to continue teaching during the COVID-19 pandemic, but the effectiveness of some online courses, especially those engineering courses with experimentation activities, remains disputed. The main challenge is fostering collaborative problem solving skills for novice students as online collaboration increases their cognitive load. This research thus tapped into novice engineering students' cognitive load to develop a more granular, multimodal view of how cognitive load influences student performance in collaborative problem solving during virtual experimentation activities. The findings of this research provided significant implications for the future design and implementation of virtual laboratories in computer science engineering education.

This article proposes two innovative approaches, the one-channel learning image recognition and the three-channel learning image recognition, to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5235 students and 576 absolute/1728 relative input variables were conducted to verify their effectiveness. The results indicate that both methods can significantly capture more at-risk students (the highest average recall rate is equal to 77.26%) than the following machine learning algorithms—support vector machine, random forest, and deep neural network—in the middle of the semester. In addition, the innovative approaches allow minor subtypes of at-risk student identification and provide visual insights for personalized interventions. Implications and future directions are also discussed in this article.

This study identifies the factors that attract U.S. residents to eco-friendly events. To achieve our research objective, we obtained data (N = 1,115) from a cross-national, web-based survey distrib...

Purpose For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis. Design/methodology/approach This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach. Findings The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students. Originality/value The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

This paper reports how a commercial bank in Asia uses big data analytic as a tool to explore the internal B2B data to improve supply chain finance and the efficiency of marketing tactics and campaigns. A case study was conducted by analyzing two types of supply chain relationships: (1) supply chain relationships in the credit reports; (2) e-wiring transactions among supply chain companies. The results show that big data analytics is very useful in terms of improving the commercial banks' marketing and risk management performances. The case study also set a good example for B2B firms seeking to understand how they could leverage big data analytics to differentiate customer solutions, sustain profitability and generate new business values. Theorical and practical implications are also discussed.

Purpose Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown. Design/methodology/approach This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance. Findings Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences. Research limitations/implications A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns. Practical implications The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs. Originality/value This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching ...

The study proposes two new measures, time and location entropy, to depict students’ physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been ...

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses.