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We are honored to receive a best paper award at the ASEE Mid-Atlantic Fall 2020 Conference for our paper entitled “Impact of Open Education Resources (OER) on Student Academic Performance and Retention Rates in Undergraduate Engineering Departments” with Yongchao Zhao and Cailean Cooney.
I am pleased to announce that the article titled: “Our Stories: First-year Learning Communities Students Reflections on the Transition to College”, is published in Learning Communities Research and Practice (LCRP). This article is co-authored by the FYLC (First Year Learning Community) researchers: Karen Goodlad, Jennifer Sears, Mery Diaz, Sandra Cheng and Philip Kreniske. The full article can be viewed at: https://academicworks.cuny.edu/ny_pubs/538/
Analysis of diverse first-year and first-generation learning communities students’ reflective narratives shows this population of students at an urban commuter college of technology face significant challenges in the transition into college. Designed to assist in this transition, the “Our Stories” digital writing project incorporates reflective writing in the long established, yet recently revitalized, learning communities program. Through analysis of the “Our Stories” project, we examine how the structure of our learning communities program, together with writing on an open digital platform, builds community and has the potential to positively influence students as they identify, and begin to make sense, of the social, emotional, and bureaucratic challenges in their transition into college. The role of peer mentors, faculty and administrators in this project is discussed.
I am pleased to announce that our paper titled: “Using Prescriptive Data Analytics to Reduce Grading Bias and Foster Student Success” is accepted for publication at the IEEE Frontiers in Education (FIE) conference 2019 to be held in October 16-19, 2019. This paper was co-authored by Reneta Lansiquot (City Tech) and Christine Rosalia (Hunter College). This is a seminal paper on the topic of reducing grading bias in the classroom by using data analytics. The abstract is as follows:
Abstract: This innovative practice work-in-progress paper presents our approach of using data analytics as an alternative solution to eliminate grading bias. Effective grading involves maintaining consistency among all students, irrespective of gender, race, ethnic background, and prior performance. Related work in this area has shown that prior work submitted by a student influences future scores given. Some of the popular methods used to eliminate grading bias involves grading rubrics, anonymous or blind grading, and/or computerized auto-graders. In spite of all these methods, some types of grading such as essays and projects still require subjective grading, which opens the door to conscious or unconscious bias.
Given the student data available regarding performance, colleges and universities are turning to analytic solutions to extract meaning from huge volumes of student data to help improve retention, graduation, and student performance rates. While looking at all the analytic options can be a daunting task, these analytic options can be categorized at a high level into three distinct types: (a) Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer “What has happened?”; (b) Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer “What could happen?”; and (c) Prescriptive Analytics, which use optimization and simulation algorithms to advise on possible outcomes and answer “What should we do?” In this paper, we use Prescriptive Analytics to provide students with advice on what action to take, based on a tool which predicts each student’s performance.
I am pleased to announce that our paper titled: “Using Natural Language Processing Tools on Individual Stories from First Year Students to Summarize Emotions, Sentiments and Concerns of Transition from High School to College” was accepted and presented at ASEE National Conference held in Tampa, FL between June 15th-19th, 2019. It was a project in collaboration with First Year Learning Community leaders: Karen Goodlad, Jennifer Sears, Phil Kreniske, Mery Diaz and Sandra Cheng.
Abstract: Research indicates striking disparities in college completion rates between students who are first generation and come from low-income households (FLI) as compared to continuing generation students. At New York City College of Technology, CUNY (City Tech) the majority of the student body are FLI. In the last decade, educators have made great efforts to re-shape and improve students’ First-year college experience with a focus on FLI students. One of the ten high-impact educational practices recognized nationally to improve first year student persistence and retention is First-Year Learning Communities (LC). A LC is a group of students who enroll in two or more courses, generally in different disciplines that are linked together by a common theme, in an academic semester. LCs involve cooperative learning, alternative assessment in the classroom, cross-disciplinary writing assignments, and critical thinking activities. LCs first came to our institution, City Tech, through a Title V Grant in 2000 and were adopted by the college in 2005. The academic performance of students participating in LCs at City Tech reflects national trends. When compared to the general population at the College, students in LC earn higher GPAs, have higher retention rates, and demonstrate greater satisfaction.
In order to complement the community-building efforts within learning community classrooms, we, a cohort of faculty leaders and administrators of City Tech’s First Year Learning Communities, a program offered through the college’s Office of First Year Programs, developed “Our Stories” digital writing project which extends the student’s network beyond the physical and temporal limits of class meeting times. Students in our LC were given the opportunity to share their personal stories of the transition from high school to college on a digital platform called OpenLab, a campus-wide, open digital WordPress platform for teaching, learning, and sharing. Over the course of a semester, LC students were prompted with the same prompt three times, at the beginning of the semester, roughly in the middle of the term, and in the last weeks. Peer Mentors, upper level students who, among other responsibilities, were trained to respond to “Our Stories” posts actively engaged in the project.
We analyzed student stories, using text analytics tools such as Natural Language processing (NLP) and Tone Analyzer to better understand the transition experience. The NLP analyzer helped summarize emotions and concepts, and identified some common concerns of students by identifying common keywords. The Tone Analyzer tool uses linguistic analysis to detect joy, fear, sadness, anger, analytical, confident and tentative tones found in text. Such summarizations of student stories provide suggestions to the college on how we can better orient students and prepare them for their first year. In this paper, we present top concerns of students who are transitioning from high school to college. We will also investigate through the stories if the overall experience of students gets better or worse through their first year.
I am pleased to inform that our Poster titled “A Peer based Tutoring and Mentoring Model for First Year Computer Science Courses Based on Strategies Used by Songbirds for Learning”, (with co-author L. Baron) has been accepted for publication at the 50th ACM Special Interest Group on Computer Science Education (SIGCSE ’19) to be held from February 27– March 2nd, 2019 at Minneapolis, Minnesota, USA.”
Our paper (with Dr. Candido Cabo) on “Promoting students’ social interactions results in an improvement in performance, class attendance and retention in first year computing courses” has been accepted for publication at the 48th IEEE Annual Frontiers in Education (FIE) 2018 Conference. The conference will be held in San Jose, California, from Oct 4th-6th, 2018. The program for the conference can be found at: https://edas.info/p23986
Our paper (with Dr. Janusz Kusyk and Dr. Yu-Wen Chen) on “Design of Cloud Based Robots using Big Data Analytics and Neuromorphic Computing” has been accepted for publication at the 31st IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2018). The conference will be held in Quebec city, Canada, from May 13th-16th, 2018.
I am pleased to inform that a discussion session titled “Pros and Cons of Using Data Analytics for Predicting Academic Performance in Computer Science Courses” (with co-authors J. Bivens and J. Chen), and a Poster titled “Building a Community of First Year Students Improves Student Retention and Performance in Computing Courses” (with co-author C. Cabo) are both accepted for publication at the ACM SIGCSE 2018 conference to be held in Feb 2018.
Our new paper (with Dr. Candido Cabo) on “Building Community Improves Student Performance in First-Year Computing Courses” has been accepted for presentation at the American Educational Research Association (AERA) 2018 annual meeting. AERA received over 13,000 submissions this year. The conference will be held in New York City, New York, from Friday, April 13 to Tuesday, April 17, 2018. More details about the conference can be found at: http://www.aera.net/Events-Meetings/Annual-Meeting/2018-Annual-Meeting-General-Information
Paper titled “Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis” is now published. This is work done in collaboration with Math professors (Nadia Benakli, Boyan Kostadinov, Satyanand Singh) at CityTech.
Abstract: The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data analysis. We present nine computer experiments and suggest a few more, with applications to calculus, probability and data analysis, which engage computational thinking through simulations, visualizations and data analysis. We are using the free (open-source) statistical programming language R. Our goal is to give a taste of what R offers rather than to present a comprehensive tutorial on the R language. In our experience, these kinds of interactive computer activities can be easily integrated into a smart classroom. Furthermore, these activities do tend to keep students motivated and actively engaged in the process of learning, problem solving and developing a better intuition for understanding complex mathematical concepts.