Examining students’ online interaction in a live video streaming environment using data mining and text mining

This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students’ patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students’ online participation. A correlation is found between the number of online questions students asked and students’ final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students’ learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper.     

Evaluation of the interactivity of students in virtual learning environments using a multicriteria approach and data mining

This work seeks to provide a new multicriteria approach to evaluate and classify the level of interactivity of students in learning management systems (LMS). We describe, step by step, the complete methodological development process of the evaluation model as well as detailing the results obtained when applying it to a higher education teaching experience. This research demonstrates that the combined use of multicriteria decision methodologies and data mining prove to be particularly suitable for identifying behavioural patterns of the users through the analysis of records generated in LMS. The results reveal that the behavioural patterns in LMS offer certain indicators as to students’ academic performance, although the study does not permit to state that those students who adopt passive attitudes in these spheres may necessarily produce low academic performance.     

A divide‐and‐conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub‐problem within the original problem. We propose a divide‐and‐conquer strategy for data mining using both the data‐based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub‐problem re‐evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing.     

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.