Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review.

Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

Augmenting the technology acceptance model with trust model for the initial adoption of a blockchain-based system.

BACKGROUND: In the collaborative business environment, blockchain coupled with smart contract removes the reliance on a central system and offers data integrity which is crucial when the transacting parties rely on the shared data. The acceptance of such blockchain-based systems is necessary for the continued use of the services. Despite many extensive studies evaluating the performance of blockchain-based systems, few have focused on users' acceptance of real-life applications. OBJECTIVE: The main objective of this research is to evaluate the user acceptance of a real-life blockchain-based system (BBS) by observing various latent variables affecting the development of users' attitudes and intention to use the system. It also aims to uncover the dimensions and role of trust, security and privacy alongside the primary Technology Acceptance Model (TAM)-based predictors and their causal relationship with the users' behavior to adopt such BBS. METHODS: We tested the augmented TAM with Trust Model on a BBS that comprises two subsystems: a Shopping Cart System (SCS), a system oriented towards end-users and a Data Sharing System (DSS), a system oriented towards system administrators. We set research questions and hypotheses, and conducted online surveys by requesting each participant to respond to the questionnaire after using the respective system. The main study comprises two separate sub-studies: the first study was performed on SCS and the second on DSS. Furthermore, each study data comprises initial pre-test and post-test data scores. We analyzed the research model with partial least square structural equation modelling. RESULTS: The empirical study validates our research model and supports most of the research hypotheses. Based on our findings, we deduce that TAM-based predictors and trust constructs cannot be applied uniformly to BBS. Depending on the specifics of the BBS, the relationships between perceived trust antecedents and attitudes towards the system might change. For SCS, trust is the strongest determinant of attitudes towards system, while DSS has perceived privacy as the strongest determinant of attitudes towards system. Quality of system shows the strongest total effect on intention to use SCS, while perceived usefulness has the strongest total effect on intention to use DSS. Trust has a positive significant effect on users' attitudes towards both BSS, while security does not have any significant effect on users' attitudes toward BBS. In SCS, privacy positively affects trust, but security has no significant effect on trust, whereas, in DSS, both privacy and security have significant effects on trust. In both BBS, trust has a moderating effect on privacy that correlates with attitudes towards BBS, whereas security does not have any mediating role between privacy and attitudes towards BBS. Hence, we recommend that while developing BBS, particular attention should be paid to increasing user trust and perceived privacy.

E-Commerce Shopping Motivation and the Influence of Persuasive Strategies.

Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar individuals. Research in the traits that are effective in tailoring or personalizing persuasive strategies is an ongoing research area. In the present study, we propose the use of shoppers' online shopping motivation in tailoring six commonly used influence strategies: scarcity, authority, consensus, liking, reciprocity, and commitment. We aim to identify how these influence strategies can be tailored or personalized to e-commerce shoppers based on the online consumers' motivation when shopping. To achieve this, a research model was developed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and tested by conducting a study of 226 online shoppers. The result of our structural model suggests that persuasive strategies can influence e-commerce shoppers in various ways depending on the shopping motivation of the shopper. Balanced buyers-the shoppers who typically plan their shopping ahead and are influenced by the desire to search for information online-have the strongest influence on commitment strategy and have insignificant effects on the other strategies. Convenience shoppers-those motivated to shop online because of convenience-have the strongest influence on scarcity, while store-oriented shoppers-those who are motivated by the need for social interaction and immediate possession of goods-have the strongest influence on consensus. Variety seekers-consumers who are motivated to shop online because of the opportunity to search through a variety of products and brands, on the other hand, have the strongest influence on authority.

Investigation of persuasive system design predictors of competitive behavior in fitness application: A mixed-method approach.

Fitness applications aimed at behavior change are becoming increasingly popular due to the global prevalence of sedentary lifestyles and physical inactivity, causing countless non-communicable diseases. Competition is one of the most common persuasive strategies employed in such applications to motivate users to engage in physical activity in a social context. However, there is limited research on the persuasive system design predictors of users' susceptibility to competition as a persuasive strategy for motivating behavior change in a social context. To bridge this gap, we designed storyboards illustrating four of the commonly employed persuasive strategies (reward, social learning, social comparison, and competition) in fitness applications and asked potential users to evaluate their perceived persuasiveness. The result of our path analysis showed that, overall, users' susceptibilities to social comparison (ßT = 0.48, p < 0.001), reward (ßT = 0.42, p < 0.001), and social learning (ßT = 0.29, p < 0.01) predicted their susceptibility to competition, with our model accounting for 41% of its variance. Social comparison partially mediated the relationship between reward and competition, while social learning partially mediated the relationship between social comparison and competition. Comparatively, the relationship between reward and social learning was stronger for females than for males, whereas the relationship between reward and competition was stronger for males than for females. Overall, our findings underscore the compatibility of all four persuasive strategies in a one-size-fits-all fitness application. We discuss our findings, drawing insight from the comments provided by participants.

GaTO: An Ontological Model to Apply Gamification in Intelligent Tutoring Systems.

Intelligent Tutoring Systems (ITSs) are concerned with the use of artificial intelligence techniques for performing adaptive tutoring to learners' according to what they know about the domain. Researchers are increasingly interested in applying gamification in e-learning systems to engage students and to drive desired learning behaviors. However, little attention has been drawn to the effective application of gamification in ITS, and how to connect theories of both concepts in a standard and formal way. Moreover, gamified ITS should manipulate a huge amount of knowledge regarding several models, i.e., gamification, domain, student and pedagogical models. Formally connecting such theories as well as representing system's knowledge relies on the use of ontologies. In this paper, we present an ontological model that connects gamification and ITS concepts. Our model takes advantage of ontologies to allow automated reasoning (e.g., on the domain, student, pedagogical or gamification models), to enable interoperability, and create awareness about theories and good practices for the designers of gamified ITS. To evaluate our model, we use an ontology evaluation method based on five knowledge representation roles. We also illustrate how it could support the development of an intelligent authoring tool to design gamified ITS.

Social cognitive determinants of exercise behavior in the context of behavior modeling: a mixed method approach.

Research has shown that persuasive technologies aimed at behavior change will be more effective if behavioral determinants are targeted. However, research on the determinants of bodyweight exercise performance in the context of behavior modeling in fitness apps is scarce. To bridge this gap, we conducted an empirical study among 659 participants resident in North America using social cognitive theory as a framework to uncover the determinants of the performance of bodyweight exercise behavior. To contextualize our study, we modeled, in a hypothetical context, two popular bodyweight exercise behaviors - push ups and squats - featured in most fitness apps on the market using a virtual coach (aka behavior model). Our social cognitive model shows that users' perceived self-efficacy (ßT = 0.23, p < 0.001) and perceived social support (ßT = 0.23, p < 0.001) are the strongest determinants of bodyweight exercise behavior, followed by outcome expectation (ßT = 0.11, p < 0.05). However, users' perceived self-regulation (ßT = -0.07, p = n.s.) turns out to be a non-determinant of bodyweight exercise behavior. Comparatively, our model shows that perceived self-efficacy has a stronger direct effect on exercise behavior for men (ß = 0.31, p < 0.001) than for women (ß = 0.10, p = n.s.). In contrast, perceived social support has a stronger direct effect on exercise behavior for women (ß = 0.15, p < 0.05) than for men (ß = -0.01, p = n.s.). Based on these findings and qualitative analysis of participants' comments, we provide a set of guidelines for the design of persuasive technologies for promoting regular exercise behavior.

Developing Culturally Relevant Design Guidelines for Encouraging Physical Activity: a Social Cognitive Theory Perspective.

The prevalence of physical inactivity and non-communicable diseases is on the rise worldwide. This calls for a systematic approach in addressing the problem, which is almost becoming a global epidemic. Research has shown that theory-driven interventions are more likely to be effective than uninformed interventions. However, research on the determinants of physical activity and the moderating effect of culture is scarce. To bridge this gap, we conducted a large-scale comparative study of the determinants of physical activity among 633 participants from individualist and collectivist cultures. Using the Social Cognitive Theory, a widely applied behavioral theory in health interventions, we modeled the determinants of physical activity for each culture and mapped them to implementable strategies in the application domain. Our structural equation model shows that, in the individualist culture, Self-Efficacy (ßT = 0.55, p < 0.001) and Self-Regulation (ßT = 0.33, p < 0.001) are the strongest determinants of Physical Activity. However, in the collectivist culture, Social Support (ßT = 0.42, p < 0.001) and Outcome Expectation (ßT = 0.11, p < 0.01) are the strongest determinants of Physical Activity. We discussed these findings, mapped the respective behavioral determinants to the corresponding persuasive strategies in the health domain and provided a set of general design guidelines for tailoring the strategies to the respective cultures.

Towards an effective health interventions design: an extension of the health belief model.

INTRODUCTION: The recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R(2) < 0.21 on average) of existing HBM's variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model. METHODS: To achieve the objective of this paper, we conducted a quantitative study of 576 participants' eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants - perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy - using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects. RESULTS: The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting healthy behavior and in informing health intervention design. The results from examining possible relationships between the determinants in our model lead to an interesting discovery of some mediating relationships between the HBM's determinants, therefore, shedding light on some possible combinations of determinants that could be employed by intervention designers to increase the effectiveness of their design. CONCLUSION: Consideration of future consequences, self-identity, concern for appearance, perceived importance, self-efficacy, perceived susceptibility are significant determinants of healthy eating behavior that can be manipulated by healthy eating intervention design. Most importantly, the result from our model established the existence of some mediating relationships among the determinants. The knowledge of both the direct and indirect relationships sheds some light on the possible combination rules.