Data Mining in Online Professional Development Program Evaluation: An Exploratory Case Study

This case study explored the potential applications of data mining in the educational program evaluation of online professional development workshops for pre K-12 teachers. Multiple data mining analyses were implemented in combination with traditional evaluation instruments and student outcomes to determine learner engagement and more clearly understand the relationship between logged activities and learner experiences. Data analysis focused on the following aspects: 1) Shared learning characteristics, 2) frequent learning paths, 3) engagement prediction, 4) expectation prediction, 5) workshop satisfaction prediction, and 6) instructor quality prediction. Results indicated that interaction and engagement were important factors in learning outcomes for this workshop. In addition, participants who had online teaching experience could be expected to have a higher engagement level but prior online learning experience did NOT show a similar relationship

The Validation of an Instrument for Evaluating the Effectiveness of Professional Development Program on Teaching Online

Attending professional development (PD) on teaching online is becoming popular for teachers in today’s K-12 online education. Due to the unique characteristics of the online instructional environments, surveys become the most feasible approach to evaluate the effectiveness of PD programs. However, there is no validated, open-access instrument available to satisfy the needs. Purpose of this study is to conduct construct validity, content validity, concurrent validity, and reliability tests on an open-access instrument for K–12 PD for online teaching. With the exception of a few items that have minor issues on content and construct validity, results show that the survey is, in general, a valid and reliable instrument. Suggestions and potential applications of the instrument are also discussed

Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching

This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students’ learning behaviors in an online business course in Taiwan. The study scientifically identified students’ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers

Impact of Big Data Analytics on Banking: A Case Study

Purpose – The 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/approach – Guided 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\u27s 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. Findings – The 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\u27 response rates, and the product affinity prediction model could boost efficient transaction and lower time costs. Originality/value – For 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\u27 competitive advantages from the aspect of TCT

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward

An Educational Data Mining Model for Online Teaching and Learning

This study contains two major parts. First, this study proposed a generic model for Educational Data Ming (EDM) studies by reviewing EDM literature and the existing data mining model. Second, the procedures of the EDM model are demonstrated with a case study approach. The case study results showed patterns and relationships discovered from the EDM model that could be applied to improve online teaching and learning and to predict students’ academic performances

An Empirical Study of Guarantee in Service E-Commerce

Service e-Commerce (SeC) is emerging as a booming form of e-commerce where various services are contracted, managed, sold, and even delivered via the Internet. However, the uncertainty of service quality due to information asymmetry has been a major challenge to the development of SeC. Some SeC platforms tried to promote service business by lowering buyer’s perceived risk through the service guarantee mechanism. However, the mechanism seems not very successful to lift the low participation rate. This study investigated the effects of service guarantee on service e-marketplace by examining the case of zhubajie.com, a well-known service e-marketplace in China. A total of 30,406 providers (including 406 service-guarantee and 30,000 non-service-guarantee providers) were collected and analyzed. The analyses found that there are different modes for low-reputation and high-reputation service providers to participate in the service guarantee. In addition, results also show that service guarantee only improves business performance for the service providers with high reputation. For low-reputation service providers, the service guarantee mechanism does not have significant effects. Implications and suggestions were made to guide future practice and research in similar contexts

Online Collaborative Learning in a Project-Based Learning Environment in Taiwan: A Case Study on Undergraduate Students’ Perspectives

This case study investigated undergraduate students’ first experience in online collaborative learning in a project-based learning (PBL) environment in Taiwan. Data were collected through interviews of 48 students, instructor’s field notes, researchers\u27 online observations, students’ online discourse and group artifacts. The findings revealed interesting phenomena as results of cultural influences as well as educational system impacts. Students experienced first handed various learning benefits of PBL in the intensive six-week period, yet voiced serious concerns about the changed role of the instructor, as well as strong reservations on peer collaboration as a result of the competitive tradition in education. Obviously, online collaborative learning and PBL critically challenged some culturally-rooted traditions in Taiwan. The study generates practical insights into the applications of online collaborative learning and PBL in Taiwan\u27s higher education as well as implications for cross-cultural implementation of online learning

Computer-Based Instruction and Cognitive Load

Following cognitive load theory, we used a computer-based software training paradigm to determining the optimal number of steps or information chunks to present before practice opportunities. Results demonstrating that the size of information chunks presented and the type of practice used individually influenced participants\u27 ability to effectively learn via computer-based instruction. These findings contribute to the literature by showing the importance of practice and optimal segment sizes for learning via a computer

S-KMN: Integrating Semantic Features Learning and Knowledge Mapping Network for Automatic Quiz Question Annotation

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features