How users' perceived prosumption activities influence co-creation experiences and co-creation intentions?

PurposeThe paper aims to explore how perceived prosumer content quality (PPCQ) and perceived interaction quality (PIQ) improve users' co-creation experiences and subsequently influence their co-creation intentions in the future. In addition, the paper examines users' prosumer ability into consideration.Design/methodology/approachThe research model based on stimulus-organism-response (S-O-R) paradigm is developed to observe users' participation in value co-creation activities. In total, 318 valid responses were collected from a survey. Structural equation modeling was used to examine the model and Statistical Package for the Social Sciences (SPSS) PROCESS macro (Model 58) by Hayes was applied to investigate the moderating effect of prosumer ability in mediation paths.FindingsIt is observed that co-creation intention is determined by user-learning value, social-integrative value and hedonic value, which are influenced by PPCQ and PIQ. Besides, uses' prosumer ability moderates the indirect effects of PPCQ and PIQ on co-creation intentions through co-creation experiences.Research limitations/implicationsThe paper provides a prosumption perspective to explain users' co-creation intentions in social commerce and proposes the importance of user-learning, social-integrative and hedonic values in determining co-creation intentions.Practical implicationsSocial commerce platforms can encourage prosumption activities and cultivate multi-level prosumers to achieve a win–win situation.Originality/valueLittle prior research has explicitly examined how and why users participate in value co-creation activities in social commerce from prosumption perspective. The current paper seeks to fill this gap and open new avenues for other value co-creation researchers.

An Automated Stable Personalised Partner Selection for Collaborative Privacy Education

E-learning platforms have never been as in-demand as they are now since the recent pandemic making privacy education more important than ever. However, for the most part, these platforms are single-user learning environments and lack student-student interactions. To overcome this deficiency, we propose a collaborative e-learning platform for privacy education that matches students in a stable and automatic manner according to students’ preferences. Each student is represented by a vector profile that is created from behavioural skills and academic knowledge obtained from the platform. Once the preferences are determined, the residents-hospitals matching algorithm is applied to select students who will collaborate with one another. Experimental results show that the proposed model offers an effective way to create stable, thus satisfied, coalitions of students from two groups of arbitrary sizes. In addition, the automation allows students to skip the tedious process of manually selecting partners. Therefore, saving their time to collaborate on privacy education with their teammates helping them to increase their privacy awareness.

Personalized recommender system on whom to follow in Twitter

Recommender systems have been widely used in social networking sites. In this thesis, we propose a novel approach to recommend new followees to Twitter users by learning their historic friends-adding patterns. Based on a user’s past social graph and her interactions with other connected users, scores based on some of the commonly used recommendation strategies are calculated and passed into the learning machine along with the recently added list of followees of the user. Learning to rank algorithm then identifies the best combination of recommendation strategies the user adopted to add new followees in the past. Although users may not adopt any recommendation strategies explicitly, they may subconsciously or implicitly use some. If the actually added followees match with the ones suggested by the recommendation strategy, we consider users are implicitly using that strategy. The experiment using the real data collected from Twitter proves the effectiveness of the proposed approach.

Personalized recommender system on whom to follow in Twitter

Recommender systems have been widely used in social networking sites. In this thesis, we propose a novel approach to recommend new followees to Twitter users by learning their historic friends-adding patterns. Based on a user’s past social graph and her interactions with other connected users, scores based on some of the commonly used recommendation strategies are calculated and passed into the learning machine along with the recently added list of followees of the user. Learning to rank algorithm then identifies the best combination of recommendation strategies the user adopted to add new followees in the past. Although users may not adopt any recommendation strategies explicitly, they may subconsciously or implicitly use some. If the actually added followees match with the ones suggested by the recommendation strategy, we consider users are implicitly using that strategy. The experiment using the real data collected from Twitter proves the effectiveness of the proposed approach.

Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.

THE REVIEWS OF USERS OF THE DUOLINGO APPLICATION: USABILITY AND OBJECTIVITY IN THE LEARNING PROCESS

Since 2011, the Duolingo platform helps those who seek to learn a second language. Present in all popular mobile operating systems, it offers, in the Brazilian interface, the teaching of Spanish, French, and English language, being English the first to be made available in the platform. Given the platform’s mission of personalized teaching, making learning fun, and universally accessible, the research aims to analyze the opinions of users on the Google Play Store. The objective was to understand the usability process, ease of access, and the user-learning relationship of the application and to achieve it, it was used the assumptions of Human-Computer Interaction, that investigates the form of communication between people and computational systems, as well as forms of accessibility and facilitation of this communication. It will be used the quantitative methodology of the comments available on the digital store in the entire month of January 2020. The results show that 85.13% of the users rated the tool with the highest score, the scores range from 1 to 5, and that usability is a positive component that facilitates learning. It is noteworthy, though, that the tool can be used as an aid in foreign language teaching processes.

Learning Styles and Dyslexia Types – Understanding Their Relationship and its Benefits in Adaptive E-learning Systems

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.