GENERATING CLICKS: DISPLAY ADVERTISING WITHIN SOCIAL MEDIA

This research study developed a comprehensive social community prototype and tested a variety of display advertising scenarios to determine which scenario motivated social media users to click. Empirical research based upon 503 users of social media led to the development of several independent variables, which were identified in the study as display advertising scenarios. The display advertising scenarios tested in the study were video, animation, engagement, and brand familiarity, as well as the size and location of the display advertising within each social media prototype web page. Data collected identified which display advertisement scenarios motivated a social media user to click on display advertisements within the social media environment

Awareness of Blockchain Usage, Structure, & Generation of Platform’s Energy Consumption: Working Towards a Greener Blockchain

Blockchain is a disruptive information technology innovation with energy consumption. As more organizations look to implement or embrace blockchain innovations, research must focus on making the blockchain greener. This research explores the current innovative blockchain usage, structure, generations, and energy consumption. An energy consumption comparison for consensus protocols is provided along with a list of recommendations for implementing green blockchains. This paper provides a significant impact upon previous literature and aids organizations considering implementing a green blockchain

Elementary Educator\u27s Experiences with Learning Object Repository Interfaces: Layered and Non-Layered

Learning object repositories (LORs) have the potential to improve education at the elementary school level. However, elementary educators are not effectively using LORs due to usability issues regarding their interface. The goal of this dissertation was to examine elementary educator experiences regarding a layered and non-layered interface. This study was conducted over two academic quarters. A total of 49 elementary educators completed the study. Participants were divided into control and experimental groups. The groups were assigned a different LOR. Participants were asked to use the LOR and record their experiences of the LOR. Data were collected via a demographic survey, time diary, and interviews. Hypothesis 1, which examined the number of Learning Objects (LOs) incorporated into lesson plans between groups was tested using an Analysis of Variances (ANOV A); and a significant difference was found between groups. The experimental group had greater success in incorporating LORs into the lesson plans than the control group. Hypothesis 2, which examined the main effect of the number of times the LOR was used, the number of generated LOs, and the number of good quality LOs found between groups was tested using a Multiple Analysis of Variance (MANOVA); and a significant difference was found between groups. The experimental group had greater success than the control group. Additional ANOVAs were conducted to individually examine the number of times the LOR was used, the number of generated LOs, and the number of good quality LOs found between groups. No significant differences were found. Hypothesis 3, which examined the correlation between age, gender, experience, comfort level and skill level, and the number of LOs incorporated into lesson plans, was tested using an Intra Class Correlation (ICC); no significant difference was found between groups. Data gathered through interviews were analyzed using the constant comparison method. The qualitative findings supported the quantitative findings and helped to identify additional factors including time and limited LOR knowledge that are affecting the usage of the LORs

Principle Component Analysis for Feature Reduction and Data Preprocessing in Data Science

Medical datasets are large and complex. Due to the number of variables contained within medical data, machine learning algorithms may not be able to induct patterns from the data or may over fit the learned model to the data thereby reducing the generalizability of the model. Feature reduction seeks to limit the number of variables as input by establishing correlations between variables and reducing the overall feature set to the minimum number of possible variables to describe the data. This research seeks to examine the effects of principal component analysis for feature reduction when applied to decision trees. Results indicate that principle component analysis (PCA) may be employed to reduce the number of features; however, the results suffer minor degradation

Parental Perceptions and Recommendations of Computing Majors: A Technology Acceptance Model Approach

Currently, there are more technology related jobs then there are graduates in supply. The need to understand user acceptance of computing degrees is the first step in increasing enrollment in computing fields. Additionally, valid measurement scales for predicting user acceptance of Information Technology degree programs are required. The majority of existing research regarding methods for increasing enrollment focus on subjective measures that are often invalid or invalidated. This research study adapts a well-known, validated and established user acceptance of information technology model (TAM) developed by Davis in 1989. The TAM model was adapted to understand factors for the acceptance of information technology and was based on the long standing Theory of Reasoned Action from behavioral psychology. This work adapts TAM to explore factors that influence parents’ decision to recommend Information Technology as a Major to their children. Since parents have a high degree of influence over the major selection of their children, determining factors for recommending I.T. as a major can assist I.T. programs in improved marketing to increase enrollment. Results revealed parent’s perception of the perceived usefulness of I.T. affected their willingness to recommend I.T. as a major to their children. Additionally, parents were not concerned with how easy I.T. is but, they were concerned with the usefulness of I.T

Evaluating the Effectiveness of Student Group Work for Mobile Application Development Learning

Programming is often a challenge to teach to technology degree students but, it is even more challenging to teach programming to a mix of interdisciplinary students. As such, instructors strive to improve their lessons and methods to encompass a broad range of students, including those students who never took a programming course. In an attempt to improve student learning and increase student learning outcomes within an applications/programming course, group work was examined. Specifically, this study was conducted to understand students’ perceptions regarding the effectiveness of the student’s group/team experience and learning outcomes when developing a mobile application. Results were favorable towards using group work for mobile application development learning, productivity, enjoyment and confidence of quality

A Comparison of Open Source Tools for Data Science

The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects

A Comparison of Open Source Tools for Data Science

The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects

Parental Perceptions and Recommendations of Computing Majors: A Technology Acceptance Model Approach

Currently, there are more technology related jobs then there are graduates in supply. The need to understand user acceptance of computing degrees is the first step in increasing enrollment in computing fields. Additionally, valid measurement scales for predicting user acceptance of Information Technology degree programs are required. The majority of existing research regarding methods for increasing enrollment focus on subjective measures that are often invalid or invalidated. This research study adapts a well-known, validated and established user acceptance of information technology model (TAM) developed by Davis in 1989. The TAM model was adapted to understand factors for the acceptance of information technology and was based on the long standing Theory of Reasoned Action from behavioral psychology. This work adapts TAM to explore factors that influence parents’ decision to recommend Information Technology as a Major to their children. Since parents have a high degree of influence over the major selection of their children, determining factors for recommending I.T. as a major can assist I.T. programs in improved marketing to increase enrollment. Results revealed parent’s perception of the perceived usefulness of I.T. affected their willingness to recommend I.T. as a major to their children. Additionally, parents were not concerned with how easy I.T. is but, they were concerned with the usefulness of I.T

Evaluating the Effectiveness of Self-Created Student Screencasts as a Tool to Increase Student Learning Outcomes in a Hands-On Computer Programming Course

Computer programming is challenging to teach and difficult for students to learn. Instructors have searched for ways to improve student learning in programming courses. In an attempt to foster hands-on learning and to increase student learning outcomes in a programming course, the authors conducted an exploratory study to examine student created screencasts and their impact on students’ performance regarding specific learning outcomes in a hands-on programming course. This study was conducted over four semesters when an instructor taught two sections of the course per semester; one section generated self created student screencasts in-class and the other section did not. The subjects were undergraduate business students enrolled in an upper level applications/programming course at a university in Pennsylvania State System of Higher Education system. The experimental method was used to compare the differences in graded classroom activities, theory assessments, lab assessments, and final exam scores between the classes. Results showed that students who created screencasts while following along with the instructors step by step programming instructions as well as created screencast while independently working significantly (