Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

User-Based Web Recommendation System: A Case Study of the National Museum of History

With the explosion and the rapidly growing market of the Internet, it is imperative that managers re-think to using technology, especially internet, to deliver services faster, cheaper, and with better quality than their competitors do. The web site provides a communication way that reveals real-time assess data and fruitful information of customers. Therefore, the call for customer with personalized web pages has become loud. To achieve personalized web pages, this study proposes recommendation algorithm of user behavior oriented by using the web log files from National Museum of History

Detecting Structure In Chaos: A Customer Process Analysis Method

Detecting typical patterns in customer processes is the precondition for gaining an understanding about customer issues and needs in the course of performing their processes. Such insights can be translated into customer-centric service offerings that provide added value by enabling customers to reach their process objectives more effectively and rapidly, and with less effort. However, customer processes performed in less restrictive environments are extremely heterogeneous, which makes them difficult to analyse. Current approaches deal with this issue by considering customer processes in large scope and low detail, or vice versa. However, both views are required to understand customer processes comprehensively. Therefore, we present a novel customer process analysis method capable of detecting the hidden activity-cluster structure of customer processes. Consequently, both the detailed level of process activities and the aggregated cluster level are available for customer process analysis, which increases the chances of detecting patterns in these heterogeneous processes. We apply the method to two datasets and evaluate the results’ validity and utility. Moreover, we demonstrate that the method outperforms alternative solution technologies. Finally, we provide new insights into customer process theory

A Review of Subsequence Time Series Clustering

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies

Recommending Best Products from E-commerce Purchase History and User Click Behavior Data

E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has purchased recently. This matrix is usually sparse and does not provide a lot of information about customer purchases or product clickstream behavior (eg., clicks, basket placement, and purchase) history, which possibly can improve product recommendations accuracy. Existing recommendation systems in E-commerce with clickstream data include those referred in this thesis as Kim05Rec, Kim11Rec, and Chen13Rec. Kim05Rec forms a decision tree on click behavior attributes such as search type and visit times, discovers the possibility of a user putting products into the basket and uses the information to enrich the user-item rating matrix. If a user clicked a product, Kim11Rec then finds the associated products for it in three stages such as click, basket and purchase, uses the lift value from these stages and calculates a score, it then uses the score to make recommendations. Chen13Rec measures the similarity of users on their category click patterns such as click sequences, click times and visit duration; it then can use the similarity to enhance the collaborative filtering algorithm. However, the similarity between click sequences in sessions can apply to the purchases to some extent, especially for sessions without purchases, this will be able to predict purchases for those session users. But the existing systems have not integrated it, or the historical purchases which shows more than whether or not a user has purchased a product before. In this thesis, we propose HPCRec (Historical Purchase with Clickstream based Recommendation System) to enrich the ratings matrix from both quantity and quality aspects. HPCRec firstly forms a normalized rating-matrix with higher quality ratings from historical purchases, then mines consequential bond between clicks and purchases with weighted frequencies where the weights are similarities between sessions, but rating quantity is better by integrating this information. The experimental results show that our approach HPCRec is more accurate than these existing methods, HPCRec is also capable of handling infrequent cases whereas the existing methods can not