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Tag Archives: Big Data Analytics
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 announce that I have been awarded the 2019-2020 Cycle 50 PSC-CUNY Research Award (Traditional A) for research in “Using Data Analytics for Personalization of Online Tutoring Systems” to begin July 1st 2019.
I will be giving the Keynote talk at the 12th City Tech Research Conference 2018 on May 1st. The title of my talk is: “The Power of Descriptive, Predictive and Prescriptive Data Analytics”. I will be discussing the simplicity and power of large amounts of data in two specific areas: Search Engines and Educational Data Mining. The conference will be held in N-119. The schedule for the conference is: CTRC-12th City Tech Research Conference-PC-D.