Information Technology (IT) high school learners are constantly struggling to cope with the challenges of succeeding in the subject. IT teachers, therefore, need to be empowered to utilize appropriate teaching–learning strategies to improve IT learners’ success in the subject. By promoting critical thinking skills, IT learners have the opportunity to achieve greater success in the most difficult part of the curriculum, which is programming. Participating IT teachers received once-off face-to-face professional development where some teachers received professional development in critical thinking strategies while other IT teachers received professional development in critical thinking strategies infused into pair programming. To determine how teachers experience these suggested strategies, teachers participated in initial interviews as well as follow-up interviews after they had implemented the suggested strategies. From the interviews, it became evident that teachers felt that their learners benefited from the strategies. Teachers in the pair programming infusing critical thinking strategies focused more on the pair programming implementation than on the totality of pair programming infused with critical thinking. Although teachers were initially willing to change their ways, they were not always willing to implement new teaching–learning strategies.

An algorithm learning tool was developed for an introductory computer science class in a specialized science and technology high school in Japan. The tool presents lessons and simple visualizations that aim to facilitate teaching and learning of fundamental algorithms. Written tests and an evaluation questionnaire were designed and implemented along with the learning tool among the participants. The tool’s effect on the learning performance of the students was examined. The differences of the two types of visualizations offered by the tool, one with more input and control options and the other with fewer options, were analyzed. Based on the evaluation questionnaire, the scales with which the tool can be assessed according to its usability and pedagogical effectiveness were identified. After using the algorithm learning tool there was an increase in the posttest scores of the students, and those who used the visualization with more input and control options had higher scores compared to those who used the one with limited options. The learning objectives used to evaluate the tool correlated with the test performance of the students. Properties comprised of learning objectives, algorithm visualization characteristics, and interface assessment are proposed to be incorporated in evaluating an algorithm learning tool for novice learners.

Internship courses, unlike others, are multi-pronged because they require coordination at different levels. Typically, a faculty member coordinates the communication and implementation at each level to achieve the desired outcomes. We call the position that this faculty holds the “internship coordinator”. For the work of the internship coordinator to be successful, he/she may need to synchronize the work of the internship with all parties involved. Failure to coordinate at one level or another may affect the work of other parties involved in completing the internship for the students. This paper explains the experience of an internship program at the computer science department (COSC) at Indiana University Indiana University of Pennsylvania (IUP). We focus on the work of the internship coordinator for this program and his work to communicate and coordinate to successfully implement the internship experience for the students. We first discuss the theoretical foundation that led to the development of internship programs in academia and then elaborate on the multiple levels of the role of the internship coordinator in completing the internship experience for the students.

One effective way of accelerating the commercialization of university innovations (inventions) is to execute a “Technological Entrepreneurship” framework that helps the execution of agreements between universities and industry for commercialization. Academics have been encouraged to commercialize their research and findings yet the level of success of commercialization of inventions (innovations) in industry is questionable. As there is no agreed commercialization framework to guide the execution of processes to support inventions moving from laboratories to the right market. The lack of capabilities of appropriate processes have undermined the turning of innovation and products into wealth. The research questions are designed to identify the constraints and hindrances of commercialization and the characteristics of successful processes built from framework based on selected case studies of incubation capabilities within universities commercialization program.

Aim/Purpose: This study aims to investigate various characteristics from both victims as defaced websites and defacers that linked to a risk of being defaced through a set of descriptive analysis. Background: The current figures from a spectrum of sources, both academic and non-academic reports, proved a progressive increase of website defacement attacks to numerous organisations. Methodology: This study obtains a set of data from Zone-H site, which is accessible to the public, including 99,437 defaced websites. The descriptive analysis is applied in order to understand the motives of defacers and the probability of website re-defacements through the statistical investigation. Findings: The motives for defacing websites are driven mainly due to entertaining reasons. This in turn has an implication on the type of techniques defacers attack websites.

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: Medical images are very sensitive data that can be transferred to medical laboratories, professionals, and specialist for referral cases or consultation. Strict security measures must be utilized to keep these data secured in computer networks when transferred to another party. On a daily basis, unauthorized users derive ways to gain access to sensitive patient medical information. Background: One of the best ways to which medical image could be kept secured is through the use of quantum cryptography Methodology : Applying the principles of quantum mechanics to cryptography has led to a remarkable new dimension in secured network communication infrastructure. This enables two legitimate users to produce a shared secret random bit string, which can be used as a key in cryptographic applications, such as message encryption and authentication. Contribution: This paper can make it possible for the healthcare and medical professions to construct cryptographic communication systems to keep patients’ transferred data safe and secured. Findings: This work has been able to provide a way for two authorized users who are in different locations to securely establish a secret network key and to detect if eavesdropping (a fraudulent or disruption in the network) has occurred Recommendations for Practitioners: This security mechanism is recommended for healthcare providers and practitioners to ensure the privacy of patients’ medical information. Recommendation for Researchers: This paper opens a new chapter in secured medical records Impact on Society Quantum key distribution promises network security based on the fundamental laws of quantum mechanics by solving the problems of secret-key cryptography . Future Research: The use of post-quantum cryptography can be further researched.

Aim/Purpose: This paper investigates the changing paradigms for technical skills that are needed by Data Engineers in 2018. Background: A decade ago, data engineers needed technical skills for Relational Database Management Systems (RDBMS), such as Oracle and Microsoft SQL Server. With the advent of Hadoop and NoSQL Databases in recent years, Data Engineers require new skills to support the large distributed datastores (Big Data) that currently exist. Job demand for Data Scientists and Data Engineers has increased over the last five years. Methodology: This research methodology leveraged the Pig programming language that used MapReduce software located on the Amazon Web Services (AWS) Cloud. Data was collected from 100 Indeed.com job advertisements during July of 2017 and then was uploaded to the AWS Cloud. Using MapReduce, phrases/words were counted and then sorted. The sorted phrase / word counts were then leveraged to create the list of the 20 top skills needed by a Data Engineer based on the job advertisements. This list was compared to the 20 top skills for a Data Engineer presented by Stitch that surveyed 6,500 Data Engineers in 2016. Contribution: This paper presents a list of the 20 top technical skills required by a Data Engineer.

Aim/Purpose: The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units. Background: In Sri Lankan state universities, students have been enrolled only from the Physical Science stream to do a degree program in Computer Science. In addition to that, universities have been offering some course units in Mathematics to provide the required mathematical maturity to Computer Science undergraduates. Despite of this it is observed that the failure rates in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. Methodology : Academic records comprised of all 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university were considered for this study. Contribution: This study helps academics in identifying suitable curricula for Mathematics course units to improve students’ performance in theoretical Computer Science courses. Findings: Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impacted on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units. Future Research: Identifying other critical success factors contributing to the students’ academic performance of the theoretical Computer Science through empirical studies

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

Aim/Purpose: The study describes empirical research into agile Requirements Engineering (RE) practices based on an analysis of data collected in a large higher education organization. Background: Requirements Engineering (RE) in agile development contexts is considerably different than in traditional software development. The field of agile RE is still nascent where there is a need to evaluate its impact in real-world settings. Methodology: Using a case study methodology, the study involved interviewing nine experienced software practitioners who reflected on the use and implementation of various agile RE practices in two software development projects of a student management system. Contribution: The primary contribution of the paper is the evaluation of agile RE practices in a large tertiary educational organization. Based on the analysis of the data, it provides valuable insights into the practice of agile RE in a specific context (i.e., education), but just as importantly, the ones that were omitted or replaced with others and why. Findings: While the evolutionary and iterative approach to defining requirements was followed in general, not all agile practices could be fully adhered to in the case organization. Although face-to-face communication with the customers has been recognized as one the most important agile RE practices, it was one of the most difficult practices to achieve with a large and diverse customer base. Addressing people issues (e.g., resistance to change, thinking, and mindset) was found to be a key driver to following the iterative RE process effectively. Contrary to the value-based approach advocated in the literature, the value-based approach was not strictly adhered to in requirements prioritization. Continuous integration was perceived to be a more beneficial practice than prototyping, as it allows frequent integration of code and facilitates delivering working software when necessary. Recommendations for Practitioners: Our study has important implications for practitioners. Based on our empirical analysis, we provide specific recommendations for effective implementation of agile RE practices. For example, our findings suggest that practitioners could address the challenges associated with limited face-to-face communication challenges by producing flexible, accessible, and electronic documentation to enable communication. Recommendations for Researchers: Researchers can use the identified agile RE practices and their variants to per-form in-depth investigations into agile requirements engineering in other educational contexts. Impact on Society: There are a number of new technologies that offer exciting new opportunities that can be explored to maximize the benefits of agile and other requirements techniques. Future Research: Future research could conduct case studies in different contexts and thus con-tribute to developing bundles or collections of practices to improve software development processes in specific contexts.