Aim/Purpose : To examine the use of a flipped classroom in the English Language subject in secondary classrooms in Hong Kong. Background: The research questions addressed were: (1) What are teachers’ perceptions towards the flipped classroom pedagogy? (2) How can teachers transfer their flipped classroom experiences to teaching other classes/subjects? (3) What are students’ perceptions towards the flipped classroom pedagogy? (4) How can students transfer their flipped classroom experiences to studying other subjects? (5) Will students have significant gain in the knowledge of the lesson topic trialled in this study? Methodology: A total of 57 students from two Secondary 2 classes in a Band 3 secondary school together with two teachers teaching these two classes were involved in this study. Both quantitative and quantitative data analyses were conducted. Contribution: Regarding whether the flipped classroom pedagogy can help students gain significantly in their knowledge of a lesson topic, only one class of students gained statistically significantly in the subject knowledge but not for another class. Findings: Students in general were positive about the flipped classroom. On the other hand, although the teachers considered that the flipped classroom pedagogy was creative, they thought it may only be useful for teaching English grammar. Recommendations for Practitioners: Teachers thought that flipping a classroom may only be useful for more motivated students, and the extra workload of finding or making suitable pre-lesson online videos is the main concern for teachers. Recommendations for Researchers: Both quantitative and qualitative analyses should be conducted to investigate the effectiveness of a flipped classroom on students’ language learning. Impact on Society : Teachers and students can transfer their flipped classroom experiences in English Language to teaching and studying other subjects. Future Research: More classes should be involved and a longer period of time should be spent on trial teaching in which a flipped classroom can be implemented in different lesson topics, not only teaching grammar. Teachers also need to determine if students can use the target language item in a task.

Aim/Purpose: The aims of this study were to investigate the feasibility of automatic assessment of programming tasks and to compare manual assessment with automatic assessment in terms of the effect of the different assessment methods on the marks of the students. Background: Manual assessment of programs written by students can be tedious. The assistance of automatic assessment methods might possibly assist in reducing the assessment burden, but there may be drawbacks diminishing the benefits of applying automatic assessment. The paper reports on the experience of a lecturer trying to introduce automated grading. Students’ solutions to a practical Java programming test were assessed both manually and automatically and the lecturer tied the experience to the unified theory of acceptance and use of technology (UTAUT). Methodology: The participants were 226 first-year students registered for a Java programming course. Of the tests the participants submitted, 214 were assessed both manually and automatically. Various statistical methods were used to compare the manual assessment of student’s solutions with the automatic assessment of the same solutions. A detailed investigation of reasons for differences was also carried out. A further data collection method was the lecturer’s reflection on the feasibility of automatic assessment of programming tasks based on the UTAUT. Contribution: This study enhances the knowledge regarding benefits and drawbacks of automatic assessment of students’ programming tasks. The research contributes to the UTAUT by applying it in a context where it has hardly been used. Furthermore, the study is a confirmation of previous work stating that automatic assessment may be less reliable for students with lower marks, but more trustworthy for the high achieving students. Findings: An automatic assessment tool verifying functional correctness might be feasible for assessment of programs written during practical lab sessions but could be less useful for practical tests and exams where functional, conceptual and structural correctness should be evaluated. In addition, the researchers found that automatic assessment seemed to be more suitable for assessing high achieving students. Recommendations for Practitioners: This paper makes it clear that lecturers should know what assessment goals they want to achieve. The appropriate method of assessment should be chosen wisely. In addition, practitioners should be aware of the drawbacks of automatic assessment before choosing it. Recommendation for Researchers: This work serves as an example of how researchers can apply the UTAUT theory when conducting qualitative research in different contexts. Impact on Society: The study would be of interest to lecturers considering automated assessment. The two assessments used in the study are typical of the way grading takes place in practice and may help lecturers understand what could happen if they switch from manual to automatic assessment. Future Research: Investigate the feasibility of automatic assessment of students’ programming tasks in a practical lab environment while accounting for structural, functional and conceptual assessment goals.

Aim/Purpose: Bioengineering is a burgeoning interdisciplinary learning domain that could inspire the imaginations of elementary aged children but is not traditionally taught to this age group for reasons unrelated to student ability. This pilot study presents the BacToMars videogame and accompanying curricular intervention, designed to introduce children (aged 7-11) to foundational concepts of bioengineering and to the interdisciplinary nature of scientific endeavors. Background: This pilot study explores the bioengineering-related learning outcomes and attitudes of children after engaging with the BacToMars game and curriculum intervention. Methodology: This study drew on prior findings in game-based learning and applied them to a videogame designed to connect microbiology with Constructionist microworlds. An experimental comparison showed the learning and engagement affordances of integrating this videogame into a mixed-media bioengineering curriculum. Elementary-aged children (N = 17) participated in a 9-hour learning intervention, with one group of n = 8 children receiving the BacToMars videogame and the other group (n = 9) receiving traditional learning activities on the same content. Pre- and post-surveys and interview data were collected from both groups. Contribution: This paper contributes to education research on children’s ability to meaningfully engage with abstract concepts at the intersection of science and engineering through bioengineering education, and to design research on developing educational technology for introducing bioengineering content to elementary school children. Findings: Children in both groups showed improved knowledge and attitudes related to bioengineering. Children who used BacToMars showed slightly stronger performance on game-specific concepts, while children in the control condition showed slightly higher generalized knowledge of bioengineering concepts. Recommendations for Practitioners: Practitioners should consider bioengineering as a domain for meaningful, interdisciplinary learning in elementary education.. Recommendation for Researchers: Design researchers should develop playful ways to introduce bioengineering concepts accurately and to engage children’s imaginations and problem-solving skills. Education researchers should further investigate developmentally appropriate ways to introduce bioengineering in elementary education. Impact on Society: BacToMars introduces a meaningful scenario to contextualize complex con-cepts at the intersection of science and engineering, and to engage children in real-world, interdisciplinary problem solving. Future Research: Future research should explore BacToMars and bioengineering curricula for elementary-aged children in larger samples, with longer intervention times.

Aim/Purpose: This paper presents a data mining approach for analyzing responses to advanced declarative programming questions. The goal of this research is to find a model that can explain the results obtained by students when they perform exams with Constructed Response questions and with equivalent Multiple-Choice Questions. Background: The assessment of acquired knowledge is a fundamental role in the teaching-learning process. It helps to identify the factors that can contribute to the teacher in the developing of pedagogical methods and evaluation tools and it also contributes to the self-regulation process of learning. However, better format of questions to assess declarative programming knowledge is still a subject of ongoing debate. While some research advocates the use of constructed responses, others emphasize the potential of multiple-choice questions. Methodology: A sensitivity analysis was applied to extract useful knowledge from the relevance of the characteristics (i.e., the input variables) used for the data mining process to compute the score. Contribution: Such knowledge helps the teachers to decide which format they must consider with respect to the objectives and expected students results. Findings: The results shown a set of factors that influence the discrepancy between answers in both formats. Recommendations for Practitioners: Teachers can make an informed decision about whether to choose multiple-choice questions or constructed-response taking into account the results of this study. Recommendation for Researchers: In this study a block of exams with CR questions is verified to complement the area of learning, returning greater performance in the evaluation of students and improving the teaching-learning process. Impact on Society: The results of this research confirm the findings of several other researchers that the use of ICT and the application of MCQ is an added value in the evaluation process. In most cases the student is more likely to succeed with MCQ, however if the teacher prefers to evaluate with CR other research approaches are needed. Future Research: Future research must include other question formats.

Aim/Purpose: Students tend to learn best when an array of learning styles is used by instructors. The purpose of this paper is to add, to introduce, and to apply the concepts of kinesthetic learning and learning structures to university and STEM education. Background: The study applies the concept of kinesthetic learning and a learning structure called Think-Pair-Share to an experiential exercise about Moore’s Law in an introductory MIS classroom. The paper details the exercise and each of its components. Methodology: Students in two classes were asked to complete a short survey about their conceptual understanding of the course material before and after the experiential exercise. Contribution: The paper details the benefits of kinesthetic learning and learning structures and discusses how to apply these concepts through an experiential exercise used in an introductory MIS course. Findings: Results indicate that the kinesthetic learning activity had a positive impact on student learning outcomes. Recommendations for Practitioners: University educators can use this example to structure several other learning activities that apply kinesthetic learning principles. Recommendation for Researchers: Researchers can use this paper to study more about how to incorporate kinesthetic learning into education, and about teaching technology concepts to undergraduate students through kinesthetic learning. Impact on Society: The results of this study may be extremely beneficial for the university and STEM community and overall academic business community. Future Research: Researchers should consider longitudinal studies and other ways to incorporate kinesthetic learning activities into education.

Aim/Purpose: The aim of this study is to understand student’s opinions and perceptions about e-assessment when the assessment process was changed from the traditional computer assisted method to a multiple-choice Moodle based method. Background: In order to implement continuous assessment to a large number of students, several shifts are necessary, which implies as many different tests as the number of shifts required. Consequently, it is difficult to ensure homogeneity through the different tests and a huge amount of grading time is needed. These problems related to the traditional assessment based on computer assisted tests, lead to a re-design of the assessment resulting in the use of multiple-choice Moodle tests. Methodology: A longitudinal, concurrent, mixed method study was implemented over a five-year period. A survey was developed and carried out by 815 undergraduate students who experienced the electronic multiple-choice questions (eMCQ) assessment in the courses of the IS department. Qualitative analyses included open-ended survey responses and interviews with repeating students in the first year. Contribution: This study provides a reflection tool on how to incorporate frequent moments of assessment in courses with a high number of students without overloading teachers with a huge workload. The research analysed the efficiency of assessing non-theoretical topics using eMCQ, while ensuring the homogeneity of assessment tests, which needs to be complemented with other assessment methods in order to assure that students develop and acquire the expected skills and competencies. Findings: The students involved in the study appreciate the online multiple-choice quiz assessment method and perceive it as fair but have a contradictory opinion regarding the preference of the assessment method, throughout the years. These changes in perception may be related to the improvement of the question bank and categorisation of questions according to difficulty level, which lead to the nullification of the ‘luck factor’. Other major findings are that although the online multiple-choice quizzes are used with success in the assessment of theoretical topics, the same is not in evidence regarding practical topics. Therefore, this assessment needs to be complemented with other methods in order to achieve the expected learning outcomes. Recommendations for Practitioners: In order to be able to evaluate the same expected learning outcomes in practical topics, particularly in technology and information systems subjects, the evaluator should complement the online multiple-choice quiz assessment with other approaches, such as a PBL method, homework assignments, and/or other tasks performed during the semester. Recommendation for Researchers: This study explores e-assessment with online multiple-choice quizzes in higher education. It provides a survey that can be applied in other institutions that are also using online multiple-choice quizzes to assess non-theorical topics. In order to better understand the students’ opinions on the development of skills and competencies with online multiple-choice quizzes and on the other hand with classical computer assisted assessment, it would be necessary to add questions concerning these aspects. It would then be interesting to compare the findings of this study with the results from other institutions. Impact on Society: The increasing number of students in higher education has led to a raised use of e-assessment activities, since it can provide a fast and efficient manner to assess a high number of students. Therefore, this research provides meaningful insight of the stakeholders’ perceptions of online multiple-choice quizzes about practical topics. Future Research: An interesting study, in the future, would be to obtain the opinions of a particular set of students on two tests, one of the tests using online multiple-choice quizzes and the other through a classical computer assisted assessment method. A natural extension of the present study is a comparative analysis regarding the grades obtained by students who performed one or another type of assessment (online multiple-choice quizzes vs. classical computer assisted assessment).

Aim/Purpose: Despite playing a critical role in shaping the future, 70% of undergraduate engineers report low levels of motivation. Student disengagement and a lack of ownership of their learning are significant challenges in higher education, specifically engineering students in the computer science department. This study investigates the various causes of these problems among first-year undergraduate engineers. Background: Student disengagement has become a significant problem, especially in higher education, leading to reduced academic performance, lower graduation rates, and less satisfaction with learning. The study intends to develop approaches that encourage a more interesting and learner-motivated educational environment. Methodology: This research uses a mixed methods approach by combining quantitative data from a survey-based questionnaire with qualitative insights from focus groups to explore intrinsic and extrinsic motivators, instructional practices, and student perceptions of relevance and application of course content. The aim of this method is to make an all-inclusive exploration into undergraduate engineering students’ perspectives on factors contributing to this disengagement and the need for more ownership. Contribution: Inculcating passion for engineering among learners seems demanding, with numerous educational programs struggling with issues such as a lack of interest by students and no personal investment in learning. Understanding the causes is of paramount importance. The study gives suggestions to help teachers or institutions create a more engaged and ownership-based learning environment for engineering students. Findings: The findings revealed a tangled web influencing monotonous teaching styles, limited opportunities and applications, and a perceived gap between theoretical knowledge and real-world engineering problems. It emphasized the need to implement more active learning strategies that could increase autonomy and a stronger sense of purpose in their learning journey. It also highlights the potential use of technology in promoting student engagement and ownership. Further research is needed to explore optimal implementation strategies for online simulations, interactive learning platforms, and gamification elements in the engineering curriculum. Recommendations for Practitioners: It highlights the complex interplay of intrinsic and extrinsic motivation factors and the need to re-look at instructional practice and emphasize faculty training to develop a more student-centered approach. It also stresses the need to look into the relevance and application of the course content. Recommendation for Researchers: More work needs to be done with a larger, more diverse sample population across multiple institutions and varied sociocultural and economic backgrounds. Impact on Society: Enhancing learners’ educational experience can result in creating a passionate and competent team of engineers who can face future obstacles fearlessly and reduce the production of half-baked graduates unprepared for the profession’s challenges. Future Research: Conduct long-term studies to assess the impact of active learning and technology use on student outcomes and career readiness. Investigate scaling up successful strategies across diverse engineering programs. See if promising practices work well everywhere.

A well-documented architecture can greatly improve comprehension and maintainability. However, shorter release cycles and quick delivery patterns results in negligence of architecture. In such situations, the architecture can be recovered from its current implementation based on considering dependency relations. In literature, structural and semantic dependencies are commonly used software features, and directory information along with co-change/change history information are among rarely utilised software features. But, they are found to help improve architecture recovery. Therefore, we consider investigating various features that may further improve the accuracy of existing architecture recovery techniques and evaluate their feasibility by considering them in different pairs. We compared five state-of-the-art methods under different feature subsets. We identified that two of them commonly outperform others but surprisingly with low accuracy in some evaluations. Further, we propose a new subset of features that reflects more accurate user perceptions and hence, results in improving the accuracy of architecture recovery techniques.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to improve the identification accuracy, accuracy and time-consuming of traditional financial risk identification methods, this paper proposes a risk identification method for financial abnormal data based on weighted random forest. Firstly, SMOTE algorithm is used to collect abnormal financial data; secondly, the original accounting data is decomposed into features, and the features of abnormal data are extracted through random forests; then, the index weight is calculated according to the entropy weight method; finally, the negative gradient fitting is used to determine the loss function, and the weighted random forest method is used to solve the loss function value, and the recognition result is obtained. The results show that the identification accuracy of this method can reach 99.9%, the accuracy rate can reach 96.06%, and the time consumption is only 6.8 seconds, indicating that the risk identification effect of this method is good.

In view of the problems of low accuracy, long time consuming and low efficiency of the existing engineering investment risk assessment method; this paper puts forward the investment risk assessment method of power grid construction project based on grey correlation analysis. Firstly, classify the risks of power grid construction project; secondly, determine the primary index and secondary index of investment risk assessment of power grid construction project; then construct the correlation coefficient matrix of power grid project investment risk to calculate the correlation degree and weight of investment risk index; finally, adopt the grey correlation analysis method to construct investment risk assessment function to realise investment risk assessment. The experimental results show that the average accuracy of evaluating the investment risk of power grid construction projects using the method is 95.08%, and the maximum time consuming is 49 s, which proves that the method has high accuracy, short time consuming and high evaluation efficiency.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

The European Union Rights Management Directive 2014/26/EU, provides regulatory oversight of European Union (EU) Collective Management Organizations (CMOs). However, the Directive has no provision indicating how members of EU CMOs may conduct non-financial audits of their CMO income and reporting. This paper addresses the problem of a lack of an audit method through a case study of the five writer members of the music group Duran Duran, who have been members of the UK's CMO for performing rights - the Performing Rights Society (PRS) for over 35 years. The paper argues a new audit CMO member method that can address the lacunae regarding the absence of CMO member right to audit a CMO and an applicable CMO audit method.

This study investigates the nexus among the commodities: bitcoin, copper, gold, silver, crude oil, and iron ore. Previous studies on establishing the plausibility and the dynamic nexus among commodities are rare. This research attempts to fill this gap. This study investigates whether there are long-term and short-term links between commodities for the period 2010-2022 by applying the bounds testing method to co-integration and ECM, built using an ARDL model and establishing both short-term and long-term relationships among the economic variables analysed. The ECM confirmed the presence of some co-integration relationship for all the variables, both in the short and long term. A strong correlation was discovered among the commodities, which were greatly influenced by their lagged values. The results of this study provides an opportunity for policymakers and researchers to understand the nature of the relationship between the analysed variables and further support the development of new policies for economic sustainability.

Aim/Purpose: Student dropout in higher education institutions is a universal problem. This study identifies the characteristics of dropout. In addition, it develops a mathematical model to predict students who may dropout. Methodology: The paper develops a mathematical model to predict students who may dropout. The sample includes 555 freshmen in a non-profit private university. The study uses both descriptive statistics, such as cross tabulation, and a binary regression model to predict student dropout. Contribution: There are two major contributions for the paper. First, it identifies the dropout rates of each group, a finding that may be used to better allocate resources at higher education institutions. Second, it develops a predictive model that may be used in order to predict the probability of a student dropping out and take preventive actions. Findings: This study compared dropout rates of one and a half year of enrollment among Traditional Undergraduate Students. Two major findings are the following: (1) Some of the resources designed to assist student are misallocated, and (2) Predictive models can be used to calculate the probability of a student dropping out. Recommendations for Practitioners: The study recommends that institutions must create initiatives to assist freshmen students and have annual assessment to measure the success of the initiatives. Recommendation for Researchers: Two, mathematical models may be used to predict dropout rates, the paper includes a model that predicted with 66.6% accuracy students who will dropout.

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Aim/Purpose: To capture digital training experiences, the paper introduces a novel data collection method – a graphic questionnaire. It aims to demonstrate the opportunities and limitations of this tool for collecting feedback from socially disadvantaged participants of digital literacy training about their progress. Background: In training of digital skills for disadvantaged audiences through informal educational interventions, it is important to get sufficient knowledge on factors that lead to their progress in the course of training. There are many tools to measure the achievements of formal education participants, but assessing the effectiveness of informal digital skills training is researched less. The paper introduces a small-scale case study of the training programme aimed at the developing of reading and digital skills among the participants from three socially disadvantaged groups – people with hearing impairments, children from low income families, and elderly persons. The impact of the training on participants was evaluated using different tools, including a short graphic questionnaire to capture the perceptions of the participants after each training. Methodology: We performed a thematic analysis of graphic questionnaires collected after each training session to determine how the students perceived their progress in developing literacy and digital skills. Contribution The findings of the paper can assist in designing assessment of digital literacy programmes that focus not only on final results, but also on the process of gaining digital skills and important factors that facilitate progress. Findings: The graphic questionnaire allowed the researchers to get insights into the perception of acquired skills and progressive achievements of the participants through rich self-reports of attitudes, knowledge gained, and activities during training sessions. However, the graphic questionnaire format did not allow the collection of data about social interaction and cooperation that could be important in learning. Recommendations for Practitioners: Graphic questionnaires are useful and easy-to-use tools for getting rich contextual information about the attitudes, behaviour, and acquisition of knowledge in digital literacy training. They can be used in applied assessments of digital literacy training in various settings. Their simplicity can appeal to respondents; however, in the long-run interest of respondents in continuing self-reports should be sustained by additional measures. Recommendations for Researchers: Researcher may explore the variety of simple and attractive research instruments, such as “honeycomb” questionnaires and similar, to facilitate data collection and saturate feedback with significant perception of personal experiences in gaining digital literacy skills. Impact on Society: Designing effective digital literacy programmes, including engaging self-assessment methods and tools, aimed at socially disadvantaged people will contribute to their digital inclusion and to solving the issues of digital divide. Future Research: Exploration of diverse research methods and expanding the research toolset in assessing digital literacy training could advance our understanding of important processes and factors in gaining digital skills.

Aim/Purpose: Two popular methods for encouraging active learning are Augmented Reality (AR) and Virtual Field Trips (VFTs). This exploratory case study examines college students’ perceptions of a prototype AR and VFT app as an active learning strategy. Background: AR allows students to learn as they physically explore a destination, while VFTs give students the opportunity to visit exciting destinations without leaving their homes. AR and VFTs promote active learning, which has been shown to increase college student success in Science, Technology, Engineering, and Math (STEM) courses. The aim of the VFT app in this study is to provide college students in a STEM course with an interactive lesson on modeling information systems using diagrams. Methodology: This exploratory case study is intended to serve as a condensed case study performed with the prototype version of a VFT app before implementing a large-scale investigation of students’ perceptions of a more refined version of the app. The study employed a qualitative approach involving a survey with open-ended questions to gather college students’ perceptions of learning with a VFT. The data were analyzed using inductive coding. The participants are students at a mid-sized, urban, public university. Contribution: This exploratory case study serves as a proof-of-concept and starting point for other faculty who may be interested in developing their own AR and VFT apps to engage students in active learning. Releasing the app to a common Open Educational Resources (OER) repository will give other faculty easy access to re-use the app and build upon it to create their own virtual field trips. OER are learning materials that are freely available for students and faculty to download and use in their coursework. Findings: Students overwhelmingly perceived the VFT app helped them learn about the subject that was presented, citing the visual nature of the app, the real-life scenarios presented in the app, and the app’s ease of use as reasons why. The majority (over 89%) also agreed that the app motivated them to learn more about the subject, mainly due to the app’s real-life scenarios, and over 83% of students cited at least one benefit to learning with the app, such as the navigation/location features, the easy-to-use interface, and the real-world scenarios. Recommendations for Practitioners: The pedagogical implications of this study are that faculty should adopt VFTs as an active learning strategy, particularly in STEM college courses, based on the students’ positive perceptions of learning, motivation, and benefits of VFTs. Recommendations for Researchers: Researchers can expand on this exploratory case study by conducting a larger-scale study of the VFT app employed in the case study, or by developing their own VFT app based on the one in this study, to capture a broader group of students’ perceptions of VFTs as an active learning strategy. Impact on Society: The broad impact of this research on society is encouraging the adoption of VFTs as an active learning strategy since active learning strategies are shown to increase college students’ success and engagement. Future Research: Future research will be conducted in subsequent terms to gather additional data on students’ perceptions of the VFT app, as well as their perceptions of the relationship between learning and the VFT technology. Further research is also needed to survey faculty on their perceptions of how engaging with the app impacts student learning, particularly in regards to the VFT technology within the app.

Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of information systems implementations. Measurements theory and error analysis, specifically propagation of uncertainties methods, were used to derive analytical expressions for ROI errors. Monte Carlo simulation methodology was used to design and deliver a quantitative experiment to model costs and returns estimating errors and calculate ROI accuracies. Spreadsheet simulation (Microsoft Excel spreadsheets enhanced with Visual Basic for Applications) was used to implement Monte Carlo simulations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties.

Aim/Purpose: The present study investigates the relationship between the five-factor model (FFM) of personality traits and sales performance in Pakistan. Background: Personality is a well-researched area in which numerous studies have examined the correlation between personality traits and job performance. In this study, a positive effect between the various dimensions of the five-factor model (extraversion, agreeableness, conscientiousness, emotional stability, and open to experience) and sales performance in Pakistan is investigated. Methodology: Pearson’s correlation values as well as analysis methodologies were employed to gather descriptive statistics, reliability analysis, correlation analysis, and use the analytical hierarchy process (AHP). Cronbach’s alpha value helped determine the internal consistency of the group items. Questionnaires were distributed among 600 salespersons in various cities of Pakistan from April 2015 to January 2016. Subsequently, 510 questionnaires were acquired for the sample. Contribution: The current study contributes to the literature on personality traits and sales performance by applying empirical evidence from sales managers in three industries of Pakistan: pharmaceutical, insurance, and electronics. Findings: The results affirmed a positive effect of the five-factor model on sales performance among various industries in Pakistan. The effect of each sub-factor from the five-factor model was examined autonomously. There is a favorable benefit to sales managers in considering FFM when making hiring decisions. Impact on Society: FFM offers important insights into personality traits that work well within Pakistani sales industry structure. Future Research: A broader rendering of the effects of FFM on sales organizations in other geographical locations around Pakistan should be considered. Additionally, an extended study should be conducted to investigate the effects of FFM on female sales employees involving religious and cultural forces within that country.

Aim/Purpose: This study aims to investigate the relationship between the knowledge management (KM) process and the performance of construction companies. The ultimate goal is to promote better efficiency and competitive advantage in the construction industry by making the best use of knowledge. Background: While attention to KM is currently on the rise, as shown by the number of studies conducted, research on KM in the construction industry of Indonesia is still very rare. However, organizational learning as the implementation of KM provides an opportunity to improve the construction industry, and thus there is an urgency to conduct research on this topic. Methodology: This study lasted for three months and used the survey method, with 100 questionnaires distributed to contractors of grade 6 and 7 on the islands of Java and Borneo in Indonesia. A total of 54 returned questionnaires were deemed complete and eligible for further analysis. Data analysis was performed using the structural equation modeling method with partial least squares (SEM PLS). Contribution: This study helps to measure the relationship value of the KM process and company performance. Findings: The results of this study indicate that the process of KM has a significant and positive influence on company performance, and there is a positive interaction in the process of KM and company performance as well. Recommendations for Practitioners: Construction companies need to perceive that activities undertaken in a construction project should always be assumed to be a KM process in order to make strategic and effective decisions that can result in improvements to customers, finance, internal business, learning, and growth. Recommendation for Researchers: Research on the KM process and information technology needs to be developed more, so that construction companies can apply this knowledge to explore problems and create solutions, resulting in methods to facilitate efficiency and effectiveness. Impact on Society: This paper helps to understand that KM activities provide initial benefits and guidance for companies that want to apply KM. Future Research: Innovative and new ideas to cultivate the KM process in the construction industry need to be explored and developed to improve the implementation.

Aim/Purpose: This study investigates the issue of knowledge acquisition among faculty members. Background: The paper reports the use of knowledge acquisition tools and reading knowledge sources by faculty members. It also identifies demographic differ-ences among participants in using knowledge acquisition tools and reading knowledge sources. Methodology: The study used an online survey-based questionnaire tool for data collection. The participants consisted of 300 faculty members from 26 academic institu-tions in UAE. Statistical tests are used to verify and validate the hypotheses. Contribution: The paper represents one of the few empirical studies conducted on knowledge acquisition among faculty members in the GCC countries. Find-ings of the study may contribute to the theoretical and practical understanding of knowledge acquisition among faculty members. Findings: Findings of the study revealed that medical faculty members read knowledge acquisition sources more than other faculty members. Likewise, IT faculty members use knowledge acquisition tools more than other faculty members. Results of the study supported stage three of knowledge acquisition proposed in the “Stage Theory of Knowledge Consumption Growth” (Mathew, 1985). The study found that journals are the most sources read by the participants while web-based training (WBT) tools are the most used knowledge acquisition tools among faculty members. Results of the study indicated significant differ-ences among faculty members of different age groups, academic ranks, aca-demic specializations, and institutional affiliation in reading knowledge sources. Likewise, findings of the study revealed significant difference among partici-pants of different academic specializations in using knowledge acquisition tools. Recommendations for Practitioners: Results of the study could be extrapolated to other faculty members in the GCC countries. Recommendation for Researchers: More researches could be done to address different issues of knowledge acquisition among faculty members. Impact on Society: Faculty reading of knowledge sources and use of knowledge acquisition tools may have direct or indirect positive impacts on innovation, creativity, and re-search productivity in any society. Future Research: It will be interesting to apply more than one data collection method in the future research.

Aim/Purpose: This study aimed to examine the relationships among compatibility, relative advantage, complexity, IT Infrastructure, security, top Management Support, financial Support, information Policies, employee engagement, customer pressure, competitive pressure, information integrity, information sharing, attitude toward adopting technology factors, and CRM adoption Background: Customer relationship management (CRM) refers to the use of the process, information, technology, and people for the management of the interactions between the organization and its customers. Therefore, there is a need for SMEs to implement CRM practices in their businesses for competitive advantage. However, in developing nations, the adoption rate of such practices remains low. This low rate may be attributed to the lack of important factors that guide CRM adoption, and as such, the present study attempts to investigate the factors affecting CRM adoption in Palestinian SMEs. This paper used the Diffusion of Innovation Theory (DOI), Resource-Based View (RBV), and Technology, Organization, and Environment Framework (TOE) framework to identify the determinant factors from the technological, organizational, environmental, and information culture perspectives. Methodology: This study uses a quantitative approach to investigate the relationships between the variables. A questionnaire was designed to collect data from 420 SMEs in Palestine. 331respondents completed and returned the survey. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. Contribution: This study contributes to both theory and practitioners by providing insights into factors that affect CRM adoption in Palestinian SMEs, which did not explore before. Future research suggestions are also provided. Findings: The results of the study prove that the adoption of CRM depends on compatibility (CMP), security (SEC), top management support (TMS), information policies (INP), financial resources (FR), employee engagement (EEN), competitive pressure (COP), customers pressure (CUP), attitude toward adopting technology (ATA), information integrity (INI), and information sharing (INS). Surprisingly, complexity (CMX), IT infrastructure (ITI), and relative advantage (RLA) do not play any role in CRM adoption in Palestine. Recommendations for Practitioners: This study provides practitioners with the important factors for CRM adoption upon its successful implementation in the context of Palestinian SMEs. Recommendation for Researchers: Our findings may be used to conduct further studies about compatibility, security, top management support, information policies, financial resources, employee engagement, competitive pressure, customers pressure, attitude toward adopting technology, information integrity, information sharing factors, and CRM adoption by using different countries, procedure, and context. Impact on Society: The proposed framework provides insights for SMEs which have significant effects for research and practice to help facilitate the adoption of CRM Future Research: The findings may also be compared to other studies conducted in different contexts and provide deeper insights into the influence of the examined contexts on the employees’ intention toward CRM adoption in banking and universities. It would be fruitful to test whether the results hold true in developed and developing countries.

Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision. Contribution: The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models. Findings: Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers. Recommendations for Practitioners: Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange. Recommendation for Researchers: The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome. Impact on Society: The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd’s perspective. Future Research: In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.