Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: This study aims to develop a theoretical model based on the SOR (Stimulus – Organism – Response) framework and GETAMEL, which cover environmental, personal, and learning quality aspects to identify factors influencing students’ acceptance of the use of LMS in high schools, especially after COVID-19 pandemic. Background: After the COVID-19 pandemic, many high schools reopened for in-person classes, which led to a decreased reliance on e-learning. The shift from online to traditional face-to-face learning has influenced students’ perceptions of the importance of e-learning in their academic activities. Consequently, high schools are facing the challenge of ensuring that LMS can still be integrated into the teaching-learning process even after the pandemic ends. Therefore, this study proposes a model to investigate the factors that affect students’ actual use of LMS in the high school environment. Methodology: This study used 890 high school students to validate the theoretical model using Structural Equation Modeling (SEM) analysis to deliver direct, indirect, and moderating effect analysis. Contribution: This study combines SOR and acceptance theory to provide a model to explain high school students’ intention to use technology. The involvement of direct, indirect, and moderating effects analysis offers an alternative result and discussion and is considered another contribution of this study from a technical perspective. Findings: The findings show that perceived satisfaction is the most influential factor affecting the use of LMS, followed by perceived usefulness. Meanwhile, from indirect effect analysis, subjective norms and computer self-efficacy were found to indirectly affect actual use through perceived usefulness as a mediator. Content quality was also an indirect predictor of the actual use of LMS through perceived satisfaction. Further, the moderating effect of age influenced perceived satisfaction’s direct effect on actual use. Recommendations for Practitioners: This study provides practical recommendations that can be useful to high schools and other stakeholders in improving the use of LMS in educational environments. Specifically, exploring the implementation of LMS in high schools prior to and following the COVID-19 outbreak can offer valuable insights into the changing educational environment. Recommendation for Researchers: The results of this study present a significant theoretical contribution by employing a comprehensive approach to explain the adoption of LMS among high school students after the COVID-19 pandemic. This contribution extends the GETAMEL framework by incorporating environmental, personal, and learning quality aspects while also analyzing both direct and indirect effects, which have not been previously explored in this context. Impact on Society: This study provides knowledge to high schools for improving the use of LMS in educational environments post-COVID-19, leading to an enhanced teaching-learning process. Future Research: This study, however, is limited to collecting responses exclusively from Indonesian respondents. Therefore, the replication of the finding needs to consider the characteristics and culture similar to Indonesian students, which is regarded as the limitation of this study.

Aim/Purpose: The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background: This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology: The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution: The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings: Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners: Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendation for Researchers: Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society: The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research: The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

Aim/Purpose: This study aims to analyze the factors that influence user addiction to AR face filters in social network applications and their impact on the online social anxiety of users in Indonesia. Background: To date, social media users have started to use augmented reality (AR) face filters. However, AR face filters have the potential to create positive and negative effects for social media users. The study combines the Big Five Model (BFM), Sense of Virtual Community (SVOC), and Stimuli, Organism, and Response (SOR) frameworks. We adopted the SOR theory by involving the personality factors and SOVC factors as stimuli, addiction as an organism, and social anxiety as a response. BFM is the most significant theory related to personality. Methodology: We used a quantitative approach for this study by using an online survey. We conducted research on 903 Indonesian respondents who have used an AR face filter feature at least once. The respondents were grouped into three categories: overall, new users, and old users. In this study, group classification was carried out based on the development timeline of the AR face filter in the social network application. This grouping was carried out to facilitate data analysis as well as to determine and compare the different effects of the factors in each group. The data were analyzed using the covariance-based structural equation model through the AMOS 26 program. Contribution: This research fills the gap in previous research which did not discuss much about the impact of addiction in using AR face filters on online social anxiety of users of social network applications. Findings: The results of this study indicated neuroticism, membership, and immersion influence AR face filter addiction in all test groups. In addition, ARA has a significant effect on online social anxiety. Recommendations for Practitioners: The findings are expected to be valuable to social network service providers and AR creators in improving their services and to ensure policies related to the list of AR face filters that are appropriate for use by their users as a form of preventing addictive behavior of that feature. Recommendation for Researchers: This study suggested other researchers consider other negative impacts of AR face filters on aspects such as depression, life satisfaction, and academic performance. Impact on Society: AR face filter users may experience changes in their self-awareness in using face filters and avoid the latter’s negative impacts. Future Research: Future research might explore other impacts from AR face filter addiction behavior, such as depression, life satisfaction, and so on. Apart from that, future research might investigate the positive impact of AR face filters to gain a better understanding of the impact of AR face filters.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

Recently, interest in the correlation between 'contract' in transaction governance and 'trust' in relational governance mechanisms has been growing. This study focuses on issues related to the evolution of contract and inter-organisational trust dynamics in transaction governance and uses mixed research method to investigate sectors related to transaction governance in Taiwan's electronics industry. The study finds higher flexibility in contract implementation to be a promoter of trust between two parties in a relationship, thereby promoting project execution efficiency in the case of Taiwanese firms. Organisational management differs between the East and West; therefore, Western firms should understand how various contractual provisions can be used to accommodate different transactions when cooperating with Taiwanese electronics companies.

In the context of US universities where student evaluations of teaching play an important role in the retention and promotion of faculty, it is important to understand what a student expects in the classroom. This study took the perspective of Perry's cognitive development scheme with the following research question: what is the Perry level of cognitive development of business students? An established survey was used at two different universities. It was found that the median was position 3, and that there was large variation in three dimensions. First is the variation across program levels. Second, there was variation across universities. This becomes an issue when instructors move to a different university and questions the possibility to transfer 'best practices'. Third, variation was found within a specific program level. This means that instructors are faced with students who, from a cognitive perspective, have different demands which are unlikely to be simultaneously met.

In this paper, two hypothetical frameworks are proposed through the application of quality function deployment (QFD) to integrate the current institutional level and program level student learning focus areas with the relevant institutional and program specific stakeholder expectations. A generic skillset proficiency expected of all the graduating students at the institutional level by the stakeholders is considered in the first QFD application example and a program specific knowledge proficiency expected at the program level by the stakeholders is considered in the second QFD application example. Operations management major/option is considered for illustration purposes at the program level. In addition, an assurance of learning based approach rooted in continuous improvement philosophy is proposed to align the stakeholder expectations with the relevant student learning outcomes at different learning tiers.

With the continuous growth and importance of data, the need for strong data protection becomes crucial. Encryption plays a vital role in preserving the confidentiality of data, and attribute-based encryption (ABE) offers a meticulous access control system based on attributes. This study investigates the integration of Fernet encryption with initialisation vector (IV) and ABE, resulting in a hybrid encryption approach that enhances both security and flexibility. By combining the advantages of Fernet encryption and IV-based encryption, the hybrid encryption scheme establishes an effective and robust mechanism for safeguarding data. Fernet encryption, renowned for its simplicity and efficiency, provides authenticated encryption, guaranteeing both the confidentiality and integrity of the data. The incorporation of an initialisation vector (IV) introduces an element of randomness into the encryption process, thereby strengthening the overall security measures. This research paper discusses the advantages and drawbacks of the hybrid encryption of Fernet and IV with ABE.

Can questions sent to Open-Educational-Resource (OER) websites such as Ask-An-Expert serve as indicators for students’ interest in science? This issue was examined using an online questionnaire which included an equal number of questions about the topics “space” and “nutrition” randomly selected from three different sources: a 5th-grade science textbook, the “Ask-An-Expert” website, and questions collected from other students in the same age group. A sample of 113 5th-graders from two elementary schools were asked to rate their interest level in finding out the answer to these questions without knowledge of their source. Significant differences in students’ interest level were found between questions: textbook questions were ranked lowest for both subjects, and questions from the open-resource were ranked high. This finding suggests that questions sent to an open-resource could be used as an indicator of students’ interest in science. In addition, the high correlation of interests expressed by students from the two schools may point to a potential generalization of the findings. This study contributes by highlighting OER as a new and promising indicator of student interest, which may help bring “student voices” into mainstream science teaching to increase student interest in science.

This study seeks to explore what factors influence students’ ICT use and web technology competence. The objectives of this study are the following: (a) To what extent do certain elements of Rogers’ (2003) Diffusion of Innovations Theory (DOI) explain students’ ICT use, (b) To what extent do personality characteristics derived from the Big Five approach explain students’ ICT use, and (c) To what extent does motivation explain students’ ICT use. The research was conducted in Israel during the second semester of the academic year 2013-14, and included two groups of participants: a group of Educational Technology students (ET) and a group of Library and Information Science students (LIS). Findings add another dimension to the importance of Rogers’ DOI theory in the fields of Educational Technology and Library and Information Science. Further, findings confirm that personality characteristics as well as motivation affect ICT use. If instructors would like to enhance students’ ICT use, they should be aware of individual differences between students, and they should present to students the advantages and usefulness of ICT, thus increasing their motivation to use ICT, in the hopes that they will become innovators or early adopters.

This study attempts to present the variety of possible uses for iPads, in the learning process. The objective is to evaluate a unique implementation model that was tried out at a teacher training college in Israel. The methodology is based on a qualitative research paradigm. The findings show that students’ use the iPads in various contexts: (a) for ongoing personal use; (b) for planning lessons; (c) for active integration in the classroom; and (d) for reading and developing content and games. These findings are summarized in a chart that shows the different uses as levels in a hierarchical taxonomy. Analysis of the iPad’s pedagogical uses may shed light on the various skills students need to acquire in order to become teachers in 21st century. In addition, understanding the various iPad uses and their frequency can affect decision-making at the level of policy in the field of implementation of the use of mobile technologies in educational institutions.

Empowered by and tethered to ubiquitous technologies, the current generation of youth yearns for opportunities to engage in self-expression and information sharing online with personal disclosure no longer governed by concepts of propriety and privacy. This raises issues about the unsafe online activities of teens and young adults. The following paper presents the findings of a study examining the social networking activities of undergraduate students and also highlights a program to increase awareness of the dangers and safe practices when using and communicating, via social media. According to the survey results, young adults practice risky social networking site (SNS) behaviors with most having experienced at least one negative consequence. Further, females were more likely than males to engage in oversharing as well as to have experienced negative consequences. Finally, results of a post-treatment survey found that a targeted program that includes flyers, posters, YouTube videos, handouts, and in-class information sessions conducted at a Mid-Atlantic Historically Black College or University (HBCU) increased student awareness of the dangers of social media as well as positively influenced students to practice more prudent online behaviors.

CAPTCHA is one of the most common solutions to check if the user trying to enter a Website is a real person or an automated piece of software. This challenge-response test, implemented in many Internet Websites, emphasizes the gaps between accessibility and security on the Internet, as it poses an obstacle for the learning-impaired in the reading and comprehension of what is presented in the test. Various types of CAPTCHA tests have been developed in order to address accessibility and security issues. The objective of this study is to investigate how the differences between various CAPTCHA tests affect user experience among populations with and without learning disabilities. A questionnaire accompanied by experiencing five different tests was administered to 212 users, 60 of them with learning disabilities. Response rates for each test and levels of success were collected automatically. Findings suggest that users with learning disabilities have more difficulties in solving the tests, especially those with distorted texts, have more negative attitudes towards the CAPTCHA tests, but the response time has no statistical difference from users without learning disabilities. These insights can help to develop and implement solutions suitable for many users and especially for population with learning disabilities.

MOOCs are open, online courses that use information technologies to enhance the learning experience and attract various people from the entire world. The current study uses the Technology Acceptance Model (TAM), as well as personal characteristics such as learning strategies, cognitive appraisal, and Kuhlthau’s (1991) model of information seeking as theoretical bases for defining factors that may influence students adopting MOOCs in their learning process, as well as describe their feelings during the learning process. The study was conducted in Israel during the 2014 academic year, and used both quantitative and qualitative techniques and involved 102 students who participated in a MOOC as part of the requirements in an offline course. They were requested to keep study diaries. The quantitative analysis revealed that perceived usefulness (PU) and perceived ease of use (PEOU) have a major influence on the intention to enroll in a MOOC. PEOU can be increased by improving the current MOOC platforms. PU can also be improved by providing content that suits the students’ needs. The qualitative analysis showed mood changes over time; the feelings of uncertainty were replaced by expressions of confidence. We found that students have different needs and expectations. Therefore, the MOOC’s platforms should provide multiple options to accommodate these needs.

Learning objects originally developed for use in online learning environments can also be used to enhance face-to-face instruction. This study examined the learning impacts of online learning objects packaged into modules and used in different contexts for undergraduate education offered on campus at three institutions. A multi-case study approach was used, examining learning impacts across a variety of course subjects, course levels (introductory and advanced undergraduate), student levels (undergraduate and graduate), and instructional goals (i.e., replacement for lecture, remediation). A repeated measures design was used, with learning data collected prior to viewing the online module, after completion of the module, and at the end of the semester. The study provided a broad examination of ways that online modules are typically used in a college classroom, as well as measured learning effectiveness based on different instructional purpose and usage contexts. Results showed the effectiveness of the modules in serving as a substitute for classroom lecture, remediation of course prerequisite material, introduction to content with follow-up lab practice, and review for final exams. In each of these cases, the use of the modules resulted in significant learning increases, as well as retention of the learning until the end of the semester.

Aim/Purpose: This study examines the influence of Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) on the use of smart phones for mobile learning by postgraduate students in University of Ibadan, Nigeria. Background: Due to the low level of mobile learning adoption by students in Nigeria, three base constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model were used as factors to determine smart phone use for mobile learning by the postgraduate students in the University of Ibadan. Methodology: The study adopted a descriptive survey research design of the correlational type, the two-stage random sampling technique was used to select a sample size of 217 respondents, and a questionnaire was used to collect data. Descriptive statistics (frequency counts, percentages, mean, and standard deviation), test of norm, and inferential statistics (correlation and regression analysis) were used to analyze the data collected. Contribution: The study empirically validated the UTAUT model as a model useful in predicting smart phone use for mobile learning by postgraduate students in developing countries. Findings: The study revealed that a significant number of postgraduate students used their smart phones for mobile learning on a weekly basis. Findings also revealed a moderate level of Performance Expectancy (???? =16.97), Effort Expectancy (???? =12.57) and Facilitating Conditions (???? =15.39) towards the use of smart phones for mobile learning. Results showed a significant positive relationship between all the independent variables and use of smart phones for mobile learning (PE, r=.527*; EE, r=.724*; and FCs, r=.514*). Out of the independent variables, PE was the strongest predictor of smart phone use for mobile learning (β =.189). Recommendations for Practitioners: Librarians in the university library should organize periodic workshops for postgraduate students in order to expose them to the various ways of using their smart phones to access electronic databases. Recommendation for Researchers: There is a need for extensive studies on the factors influencing mobile technologies adoption and use in learning in developing countries. Impact on Society: Nowadays, mobile learning is increasingly being adopted over conventional learning systems due to its numerous benefits. Thus, this study provides an insight into the issues influencing the use of smart phones for mobile learning by postgraduate students from developing countries. Future Research: This study utilized the base constructs of the UTAUT model to determine smart phone use for mobile learning by postgraduate students in a Nigerian university. Subsequent research should focus on other theories to ascertain factors influencing Information Technology adoption and usage by students in developing countries.

Aim/Purpose: This paper presents the preliminary findings of a pilot survey that sought to examine the technology uses, backgrounds, needs, interests, career goals, and professional expectations of Generation Z students enrolled at a minority serving institution in the United States Mid-Atlantic region. Background: Students entering college today are part of Generation Z born in the late 90’s through 2016. Known for their short attention spans and heightened ability to multi-task, they already outnumber millennials and are the first true digital natives born during the age of smart phone. Methodology: In the fall of 2017, an online student perception survey was piloted with stu-dents enrolled at a mid-Atlantic minority serving institution. The survey included a combination of dichotomous, Likert-scaled, and ranking questions. The survey was administered electronically using the Survey Monkey system to students following completion of core computer concepts courses and explored their technology backgrounds, skills, perceived computing self-efficacy, and the role they predict technology will play in their future career. The data was subsequently exported to Microsoft Excel and SPSS where descriptive statistical analyses were conducted. Contribution: As Generation Z descends on college campuses, with their technology domi-nated backgrounds and different communications, learning, and social prefer-ences, it is important to better understand this generation whose needs and expectations will help shape the future of higher education. Additionally, this study also provides research on a population (first-generation minority college students) that is expanding in numbers in higher education and that the litera-ture, reports is impacted negatively by the digital divide and educational inequalities. This paper is timely and relevant and helps to extend our understanding of Generation Z. Findings: The findings show that Generation Z learners enrolled in a minority-serving institution enjoy computer classes, feel that using computers comes easy to them; and perceive themselves as experts in the use of social media, mobile operating systems, using a smart phone, searching the Web, and email. Participants also reported that they want to be more technologically literate, want to be more skilled in computer software applications, and are interested in learning about cyber security. In terms of the future, most respondents also believe that their career will require them to analyze information to inform decision making. Additionally, most stated that information security will be important to their future career. Finally, the results affirmed that college computing courses remain important and that college students recognize that technology will play a crucial role in their career with employers wanting to see job applicants with strong technology skills. Recommendations for Practitioners: Generation Z learners enrolled in higher education need, and want, a wide range of technology courses available to them in order to help them meet the rapidly evolving demands of tomorrow’s workplace. Students in this study overwhelmingly see the value in enhancing their technology skills especially in such areas as computer software applications, information management, and cyber security. Recommendation for Researchers: Institutions of higher education should invest in thorough and ongoing examinations of the information and technology literacy skills, needs, and perceptions of students. Impact on Society: Understanding the interests and needs of Generation Z learners is imperative to the future of higher education. Future Research: This survey is a work in progress that is part of a pilot study that is being used to help guide a much more sizable examination of Generation Z learners.

Aim/Purpose: Most research on work-based learning and research relates to theory, including perspectives, principles and curricula, but few studies provide contemporary examples of work-based projects, particularly in the Australian context; this paper aims to address that limitation. Background: The Professional Studies Program at University of Southern Queensland is dedicated to offering advanced practice professionals the opportunity to self-direct organizational and work-based research projects to solve real-world workplace problems; two such examples in the Australian context are provided by this paper. Methodology: The paper employs a descriptive approach to analyzing these two work-based research projects and describes the mixed methods used by each researcher. Contribution: The paper provides examples of work-based research in (a) health, safety, and wellness leadership and its relation to corporate performance; and (b) investigator identity in the Australian Public Service; neither topic has been examined before in Australia and little, if anything, is empirically known about these topics internationally. Findings: The paper presents the expected outcomes for each project, including discussion of the ‘triple dividend’ of personal, organizational, and practice domain benefits; as importantly, the paper presents statements of workplace problems, needs and opportunities, status of the practice domain, background and prior learning of the researchers, learning objectives, work-based research in the practice domain, and lessons learned from research which can be integrated into a structured framework of advanced practice. Recommendations for Practitioners: This is a preliminary study of two work-based research projects in Australia; as these and other real-world projects are completed, further systematic and rigorous reports to the international educational community will reveal the granulated value of conducting projects designed to change organisations and concordant practice domains. Recommendation for Researchers: While introducing the basic elements of research methods and expected out-comes of work-based projects, examples in this paper give only a glimpse into the possible longer-term contributions such research can make to workplaces in Australia. Researchers, as a consequence, need to better understand the relationship between practice domains, research as a valuable investigative tool in workplaces, and organizational and social outcomes. Impact on Society: Work-based learning and research have been developed to not only meet the complex and changing demands of the global workforce but have been implemented to address real-world organizational problems for the benefit of society; this paper provides two examples where such benefit may occur. Future Research: Future research should focus on the investigation of triple-dividend outcomes and whether they are sustainable over the longer term.

Aim/Purpose: This article aimed to design and evaluate a pedagogical technique for altering students’ classroom digital multitasking behaviors. The technique we designed and evaluated is called course-based undergraduate research experience (CURE). With this technique, the students wrote a research article based on a multitasking experiment that the instructor conducted with the students. The students conducted a literature review, developed their own research questions, they analyzed experiment data, and presented results. This study evaluated the how the CURE contributed to student multitasking behavior change. Background: Multitasking is defined as doing more than one thing at a time. Multitasking is really the engagement in individual and discrete tasks that are performed in succession. Research showed that students multitasked very often during courses. Researchers indicated that this was a problem especially for online teaching, because when students went online, they tended to multitask. Extant research indicated that digital multitasking in class harmed student performance. Multiple studies suggested that students who multitasked spent more time finishing their tasks and made more mistakes. Regardless of students’ gender or GPA, students who multitasked in class performed worse and got a lower grade than those who did not. However, little is known about how to change students’ digital multitasking behaviors. In this study, we used the transtheoretical model of behavior change to investigate how our pedagogical technique (CURE) changed students’ digital multitasking behaviors. Methodology: Using a course-based undergraduate research experience design, a new classroom intervention was designed and evaluated through a content analysis of pre- and post-intervention student reflections. As part of the course-based undergraduate research experience design, the students conducted a literature review, developed their own research questions, they analyzed experiment data, and presented results. This study evaluated the how teaching using a course-based undergraduate research experience contributed to student multitasking behavior change. Transtheoretical model of behavior change was used to investigate how our pedagogical technique changed students’ digital multitasking behaviors. Contribution: The paper described how teaching using a course-based undergraduate research experience can be used in practice. Further, it demonstrated the utility of this technique in changing student digital multitasking behaviors. This study contributed to constructivist approaches in education. Other unwanted student attitudes and behaviors can be changed using this approach to learning. Findings: As a result of CURE teaching, a majority of students observed the negative aspects of multitasking and intended to change their digital multitasking behaviors. Sixty-one percent of the participants experienced attitude changes, namely increased negative attitude towards multitasking in class. This is important because research found that while both students and instructors believed off-task technology use hinders learning, their views differed significantly, with more instructors than students feeling strongly that students’ use of technology in class is a problem. Moreover, our study showed that with teaching using CURE, it is possible to move the students on the ladder of change as quickly as within one semester (13 weeks). Seventy-one percent of the students reported moving to a higher stage of change post-intervention. Recommendations for Practitioners: Faculty wishing to curb student digital multitasking behaviors may conduct in-class experimentation with multitasking and have their students write a research report on their findings. Course-based undergraduate research experiences may make the effects of digital multitasking more apparent to the students. The students may become more aware of their own multitasking behaviors rather than doing them habitually. This technique is also recommended for those instructors who would like to introduce academic careers as a potential career option to their students. Recommendation for Researchers: Researchers should explore changing other unwanted undergraduate student behaviors with course-based undergraduate experiences. Researchers may use the transtheoretical model of change to evaluate the effectiveness of techniques used to change behaviors. Impact on Society: The negative outcomes of digital multitasking are not confined to the classroom. Digital multitasking impacts productivity in many domains. If techniques such as those used in this article become more common, changes in multitasking intentions could show broad improvements in productivity across many fields. Future Research: This paper constitutes a pilot study due to the small convenience sample that is used for the study. Future research should replicate this study with larger and randomized samples. Further investigation of the CURE technique can improve its effectiveness or reduce the instructor input while attaining the same behavioral changes.

Aim/Purpose: This study aims to explore levels of Technological Access (ownership, access to, and usage of computer devices as well as access to Internet services) and levels of Technological Efficacy (technology related skills) as they pertain to underserved (UNS) and underrepresented (UNR) students. Background: There exists a positive correlation between technology related access, technology related competence, and academic outcomes. An increasing emphasis on expanding online education at the author’s institution, consistent with nationwide trends, means that it is unlikely that just an increase in online offerings alone will result in an improvement in the educational attainment of students, especially if such students lack access to technology and the technology related skills needed to take advantage of online learning. Most studies on levels of Technological Access and Technological Efficacy have dealt with either K-12 or minority populations with limited research on UNS and UNR populations who form the majority of students at the author’s institution. Methodology: This study used a cross-sectional survey research design to investigate the research questions. A web survey was sent to all students at the university except first semester new and first semester transfer students from various disciplines (n = 535). Descriptive and inferential statistics were used to analyze the survey data. Contribution: This research provides insight on a population (UNS and UNR) that is expanding in higher education. However, there is limited information related to levels of Technological Access and Technological Efficacy for this group. This paper is timely and relevant as adequate access to technology and technological competence is critical for success in the expanding field of online learning, and the research findings can be used to guide and inform subsequent actions vital to bridging any educational equity gap that might exist. Findings: A critical subset of the sample who were first generation, low income, and non-White (FGLINW) had significantly lower levels of Technological Access. In addition, nearly half of the survey sample used smartphones to access online courses. Technological Efficacy scores were significantly lower for students who dropped out of or never enrolled in an online course. Transfer students had significantly higher Technological Efficacy scores while independent students (determined by tax status for federal financial aid purposes) reflected higher Technological Efficacy, but at a marginally lower level of significance. Recommendations for Practitioners: Higher education administrators and educators should take into consideration the gaps in technology related access and skills to devise institutional interventions as well as formulate pedagogical approaches that account for such gaps in educational equity. This will help ensure pathways to sustained student success given the rapidly growing landscape of online education. Recommendation for Researchers: Similar studies need to be conducted in other institutions serving UNS and UNR students in order to bolster findings and increase awareness. Impact on Society: The digital divide with respect to Technological Access and Technological Efficacy that impacts UNS and UNR student populations must be addressed to better prepare such groups for both academic and subsequent professional success. Addressing such gaps will not only help disadvantaged students maximize their educational opportunities but will also prepare them to navigate the challenges of an increasingly technology driven society. Future Research: Given that it is more challenging to write papers and complete projects using a smartphone, is there a homework gap for UNS and UNR students that may impact their academic success? What is the impact of differing levels of Technological Efficacy on specific academic outcomes of UNS and UNR students?