A technology pathway program in data technology and applications

With an exponential increase in user-generated data, there is a strong and increasing demand for employees possessing both technical skills and knowledge of human behavior. Supported by funds from the National Science Foundation Division of Undergraduate Education, we have begun to address this need by developing a technology pathway program in data technology and applications at a large, minority-serving public university. As part of this program, an interdisciplinary team of faculty created a new minor in Applied Computing for Behavioral and Social Sciences. A large number of diverse students are studying behavioral and social sciences, and the ability to model human behaviors and social interactions is a highly valuable skill set in our increasingly data-driven world. Applied Computing students complete a four-course sequence that focuses on data analytics and includes data structures and algorithms, data cleaning and management, SQL, and a culminating project. Our first full cohort of students completed the Applied Computing minor in Spring 2019. To assess the success of the minor, we conduct student surveys and interviews in each course. Here, we focus on survey data from the beginning and end of the first course, given that it served as a particularly important feedback loop to optimize the course and to inform the design and execution of subsequent courses. The data reflect a significant increase in confidence in programming abilities over time, as well as a shift in attitudes about programming that more closely matches those of experts. The data did not show a significant change in mindset over time, such that students maintained a growth mindset across the semester. Finally, with respect to goals, students placed a greater emphasis on data and tech at the end of the semester, highlighting specific career paths such as user experience and human factors. In the future, we plan to administer this same survey to social science students not involved in the minor to serve as a control group and to begin exploring the large dataset obtained from other courses in the minor. We believe that embedding computing education into the social sciences is a promising means of diversifying the technical workforce and filling the need for interdisciplinary computing professionals, as evidenced by high rates of female and underrepresented minority enrollment in our courses, as well as promising shifts in student confidence, attitudes, and career goals as a result of taking Applied Computing courses

STEM Community Chairs Progress Updates Spring 2016

The following is a brief update of the activities and efforts being undertaken by UNO’s Dr. George and Sally Haddix Community Chair of STEM Education as being held by Dr. Neal Grandgenett. The goal of this position is: Position Goal: To organize, lead and inspire collaborative STEM initiatives at UNO, that cross colleges and disciplines, and that aggressively position UNO to be a true national leader in interdisciplinary STEM programs. (Curriculum, Capacity, Collaboration

A CS1 Spatial Skills Intervention and the Impact on Introductory Programming Abilities

This paper discusses the results of replicating and extending a study performed by Cooper et al. examining the relationship between students’ spatial skills and their success in learning to program. Whereas Cooper et al. worked with high school students participat- ing in a summer program, we worked with college students taking an introductory computing course. Like Cooper et al.’s study, we saw a correlation between a student’s spatial skills and their success in learning computing. More significantly, we saw that after apply- ing an intervention to teach spatial skills, students demonstrated improved performance both on a standard spatial skills assessment as well as on a CS content instrument. We also saw a correlation between students’ enjoyment in computing and improved perfor- mance both on a standard spatial skills assessment and on a CS content instrument, a result not observed by Cooper et al

Review of Measurements Used in Computing Education Research and Suggestions for Increasing Standardization

The variables that researchers measure and how they measure them are central in any area of research. Which research questions can be asked and how they are answered depends on measurement. This paper describes a systematic review of the literature in computing education research to summarize the commonly used variables and measurements in 197 papers and to compare them to best practices in measurement for human-subjects research. Characteristics of the literature that are examined in the review include variables measured (including learner characteristics), measurements used, and type of data analysis. The review illuminates common practices related to each of these characteristics and their interactions with other characteristics. The paper lists standardized measurements that were used in the literature and highlights commonly used variables for which no standardized measures exist. To conclude, this review compares common practice in computing education to best practices in human-subjects research to make recommendations for increasing rigor

Passive or active learning: the challenges of teaching distributed computing using Raspberry Pi clusters to open distance university students

Parallel and distributed computing (PDC) is now considered a threshold concept for computing, and is embedded in computing curricula across the globe. While the costs of traditional computing clusters have made developing practical activities challenging, the rise of low-cost computers, particularly the Raspberry Pi, has led to an exploration of how PDC can be taught to students using Raspberry Pi clusters. Building on this work, we report our experiences from developing a series of low-cost Raspberry Pi clusters for use with open distance university students. Based on survey results from 484 students, we argue that our work demonstrates the benefits that remote practical activities can have for teaching PDC concepts, as well as engaging students. We conclude with a discussion of two key challenges: supporting active learning through student-led programming on the clusters, and supporting lower-performing students at a distance

Computer Science at Community Colleges: Attitudes and Trends

This study aimed to understand the identity and attitude of students enrolled in computer science (CS) or programming-related course at community colleges nationwide. This study quantitatively evaluation data for estimating the relationships between students’ identity and attitudes toward computer science with prior programming experience and other demographic factors. I distributed the survey to community college faculty of computer science programs nationwide. Questions for this study were adapted from the Computing Attitude Survey developed by Weibe, Williams, Yang, & Miller (2003). Using two robust quantitative statistical methodologies, I investigated the correlations and predictability of previous programming experience, gender, race, and age with participants\u27 attitudes toward computer science. This study drew its inspiration from prior works of Dorn and Tew (2015) and Chen, Haduong, Brennan, Sonnert, and Sadler (2018), whose studies looked at previous experiences in programming with a favorable attitude toward computer science. The primary independent variable was a students’ prior programming experience. Under evaluation, the dependent variables were students\u27 programming experience and demographic characteristics such as race, gender, and age. This investigation showed a significant association between programming experience and attitude toward computer science. Among the demographic variables evaluated, students\u27 racial identity was the only factor found highly correlated with attitudes toward computer science. Future work will consider the association between participants\u27 accumulated college credit hours and specific programming language effects on computer science attitudes

Evolution of Computational Thinking Contextualized in a Teacher-Student Collaborative Learning Environment.

The discussion of Computational Thinking as a pedagogical concept is now essential as it has found itself integrated into the core science disciplines with its inclusion in all of the Next Generation Science Standards (NGSS, 2018). The need for a practical and functional definition for teacher practitioners is a driving point for many recent research endeavors. Across the United States school systems are currently seeking new methods for expanding their students’ ability to analytically think and to employee real-world problem-solving strategies (Hopson, Simms, and Knezek, 2001). The need for STEM trained individuals crosses both the vocational certified and college degreed career spectrums. This embedded multiple case study employed mixed methods data to gain insights into the pedagogical practices, curriculum, and teacher-student interactions that occurred in three teacher’s lives. The study’s teachers were all using LSU’s Introduction to Computational Thinking (ICT) curriculum and the accompanying professional development program. The cases studied demonstrated that it was possible to train a teacher with no experience in computing to be a functional novice teacher. The three teachers demonstrated a pathway of professional growth that I classify as apprehension of the novelty, transitional growth with the content, and reinforced confidence from student interactions. The teachers were challenged by embracing new project/problem based pedagogical techniques and working in a virtual environment. Teacher success was reinforced through their ability to embrace reflective thinking practices with their students. The role of contextualization was examined as a critical factor in teacher professional evolution. The results have implications for future computing curriculum development and meaningful/ successful teacher training practices

INVESTIGATING FACTORS PREDICTING EFFECTIVE LEARNING IN A CS PROFESSIONAL DEVELOPMENT PROGRAM FOR K-12 TEACHERS

The demand for K-12 Computer Science (CS) education is growing and there is not an adequate number of educators to match the demand. Comprehensive research was carried out to investigate and understand the influence of a summer two-week professional development (PD) program on teachers’ CS content and pedagogical knowledge, their confidence in such knowledge, their interest in and perceived value of CS, and the factors influencing such impacts. Two courses designed to train K-12 teachers to teach CS, focusing on both concepts and pedagogy skills were taught over two separate summers to two separate cohorts of teachers. Statistical and SWOT analyses were then performed using measures such as attitudinal surveys and knowledge assessments. Findings showed the PD program had a significant impact on the teachers, there was a positive correlation between teachers’ pre-program confidence and knowledge, and additional insights on how to deliver such PD programs more effectively. Results will help inform K-12 CS PD program design. Advisor: Leen-Kiat So

Designing personalised, authentic and collaborative learning with mobile devices: Confronting the challenges of remote teaching during a pandemic.

This article offers teachers a digital pedagogical framework, research-inspired and underpinned by socio-cultural theory, to guide the design of personalised, authentic and collaborative learning scenarios for students using mobile devices in remote learning settings during this pandemic. It provides a series of freely available online resources underpinned by our framework, including a mobile learning toolkit, a professional learning app, and robust, validated surveys for evaluating tasks. Finally, it presents a set of evidence-based principles for effective innovative teaching with mobile devices

Research-Informed Teaching in a Global Pandemic: "Opening up" Schools to Research

The teacher-research agenda has become a significant consideration for policy and professional development in a number of countries. Encouraging research-based teacher education programmes remains an important goal, where teachers are able to effectively utilize educational research as part of their work in school settings and to reflect on and enhance their professional development. In the last decade, teacher research has grown in importance across the three i’s of the teacher learning continuum: initial, induction and in-service teacher education. This has been brought into even starker relief with the global spread of COVID-19, and the enforced and emergency, wholesale move to digital education. Now, perhaps more than ever, teachers need the perspective and support of research-led practice, particularly in how to effectively use Internet technologies to mediate and enhance learning, teaching and assessment online, and new blended modalities for education that must be physically distant. The aim of this paper is to present a number of professional development open educational systems which exist or are currently being developed to support teachers internationally, to engage with, use and do research. Exemplification of the opening up of research to schools and teachers is provided in the chapter through reference to the European Union-funded Erasmus + project, BRIST: Building Research Infrastructures for School Teachers. BRIST is developing technology to coordinate and support teacher-research at a European level