Costin Chiru

Finding student-centered open learning environments on the internet

The study analyzes dialogue in virtual communities of practice (vCoP).Learning analytics are employed in the form of automated content analysis.Significant differences were found between central and peripheral vCoP members.Central participants produced and initiated more dialogue of higher quality.The employed tools appear appropriate for assessing open-ended learning environments. Starting from the socio-constructivist concepts of (virtual) community of practice (vCoP) and internet-based argumentative open-ended learning environments, this study proposes and validates two tools for automated dialogue assessment, ReaderBench and Important Moments, developed on the ground of the polyphonic social knowledge building model. The analyzed corpus was the dialogue produced by an academic vCoP with N=179 community members in 23months, and consisting of 3685 interventions in 292 text-based discussion threads. The analysis results uncovered significant differences in the discussion threads produced by central and peripheral participants, such that central participants produced more interventions with higher collaborative dialogue quality, and the discussion threads they initiated were longer and involved a larger number of participants. Moreover, based on the automated analysis result, the vCoP participants could be classified in two clusters corresponding to the well-known core-periphery structure of CoPs. These findings are consistent with those revealed by other methods, and suggest that the employed tools are appropriate for identifying virtual communities that are appropriate as open-ended learning environments. Further research and development is needed to deepen quantitative vCoP models and test communication strategies recommended to students in vCoP-based argumentative open-ended learning environments.

Continuous User Authentication Using Machine Learning on Touch Dynamics

In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.

Automatically Determining the Popularity of a Song

The purpose of this paper is to identify a connection, if such a connection exists, between the sequence of sounds and the lyrics of a melody and its popularity with the help of machine learning techniques. The melody popularity will be quantified as the number of views and number of “like” votes on the YouTube platform, where users can upload, view and vote videos. This analysis will reveal whether the two indicators from the YouTube platform are more influenced by the words or sounds of the songs. This work may help the producers from the music industry since the popularity of a melody (determined by analyzing a large set of songs) might be a very important aspect to be considered when deciding whether to make and launch a musical product or not.

Digital Services Development Using Statistics Tools to Emphasize Pollution Phenomena

This paper presents a perspective related to information service integration for pollution awareness evaluation. The proposed methodology is based on indirect information analysis as retrieved from available literature over time. A time series - type analysis highlighting usage of pollution-related terms is employed. The displayed impact of pollution is evaluated based on public awareness, exposed through digitalized available publications. Estimation techniques and tools are also employed in order to evaluate the exact impact of pollution related events on society. The proposed methodology fosters the design of improved environmental monitoring smart services, specifically addressing the development of data processing components in information sub-systems of EISs (Enterprise Information Systems).