"LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses"
Yu, who is advised by Professor Mark Warschauer, is specializing in Language, Literacy, and Technology. His research interests include learning analytics, learning sciences, instructional design, and computational modeling. He is focusing on advanced computational methods to model and decipher student learning processes from the data available in various digital learning environments, with the goal of improving instructional design based on such empirical evidence.
Dowell’s research interests encompass cognitive psychology, discourse processing, group interaction, and learning analytics, with focuses on using language and discourse to uncover the dynamics of socially significant, cognitive, and affective processes. She applies computational techniques to model discourse and social dynamics in a variety of environments including small group computer-mediated collaborative learning environments, collaborative design networks, and massive open online courses (MOOCs).
Women are traditionally underrepresented in science, technology, engineering, and mathematics (STEM). While the representation of women in STEM classrooms has grown rapidly in recent years, it remains pedagogically meaningful to understand whether their learning outcomes are achieved in different ways than male students. In this study, we explored this issue through the lens of language in the context of an asynchronous online discussion forum. We applied Linguistic Inquiry and Word Count (LIWC) to examine linguistic features of students’ reflective posting in an online chemistry class at a four-year university. Our results suggest that cognitive linguistic features significantly predict the likelihood of passing the course and increases perceived sense of belonging. However, these results only hold true for female students. Pronouns and words relevant to social presence correlate with passing the course in different directions, and this mixed relationship is more polarized among male students. Interestingly, the linguistic features per se do not differ significantly between genders. Overall, our findings provide a more nuanced account of the relationship between linguistic signals of social/cognitive presence and learning outcomes. We conclude with implications for pedagogical interventions and system design to inclusively support learner success in online STEM courses.
"Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data"
Qiujie Li is a postdoctoral researcher in the Learning Analytics Research Network (LEARN) at New York University. For her doctoral work at UCI, Li specialized in Learning, Teaching, Cognition, and Development. During her doctoral studies, she was advised by Professor Mark Warschauer and Assistant Professor Rachel Baker.
Fischer is an assistant professor of Educational Effectiveness at the Hector Research Institute of Education Sciences and Psychology at the University of Tübingen in Germany and a Research Affiliate with the UCI School of Education. Previously, he was a distinguished postdoctoral scholar at UCI Irvine working under the mentorship of Warschauer and Dean Richard Arum.
Xu researches labor market returns to different degree programs and major areas in higher education and conducts research to explore the impacts of educational programs, interventions, and instructional practice on student course performance, persistence, and degree completion. Xu is co-director of UCI's Online Learning Research Center (OIRC).
Doroudi studies educational data sciences, educational technology, and learning sciences. He is particularly interested in studying the prospects and limitations of data-driven algorithms in learning technologies, including lessons that can be drawn from the rich history of educational technology
In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success. However, little is known about the overall utility of different data sources across prediction tasks and the fairness of their predictions with respect to different subpopulations. Using data from over 2,000 college students at a large public university, we examined the utility of institutional data, learning management system (LMS) data, and survey data for accurately and fairly predicting short-term and long-term student success. We found that institutional data and LMS data both have decent predictive power, but survey data shows very little predictive utility Combining institutional data with LMS data leads to even higher accuracy than using either alone. In terms of fairness, using institutional data consistently underestimates historically disadvantaged student subpopulations more than their peers, whereas LMS data tend to overestimate some of these groups more often. Combining the two data sources does not fully neutralize the biases and still leads to high rates of underestimation among disadvantaged groups. Moreover, algorithmic biases affect not only demographic minorities but also students with acquired disadvantages. These analyses serve to inform more cost-effective and equitable use of student data for predictive analytics applications in higher education.
Ahumada-Newhart is a National Institute of Health-funded postdoctoral fellow with UCI’s Institute for Clinical and Translational Science (ICTS). Her research interests encompass child health and human development, virtual inclusion, human-computer interaction, human-robot interaction, and emerging technologies that facilitate learning, human development, and social connectedness. She is PI and Project Director of an on-going, national, multi-case study that explores the use of interactive technologies such as telehealth and telerobots for improved health services and outcomes.
For her doctoral work, Ahumada-Newhart specialized in Language, Literacy, and Technology. She was advised by Dr. Eccles and Professor Mark Warschauer.
Eccles's academic research focuses on gender-role socialization, classroom influences on student motivation, and social development in the family and school context. She is internationally recognized for her development of the expectancy-value theory of motivation and her concept of stage-environment.
Eccles is a member of the National Academy of Education, a World Scholar at the University of London, Visiting Professor at the University of Tubingen, Germany, and Research Fellow at the Australian Catholic University, Sydney, Australia. At UCI, Eccles also directs the Motivation and Identity Research Lab (MIRL).
Each year, millions of children are homebound due to illness that requires limited exposure to other children and adults due to health risks. What are the consequences of this isolation for their development and well-being, and how might robotic avatars be used to enrich their developmental experiences? These are the questions guiding this paper. Fundamental developmental theories and theories of thriving make clear the importance of exposure to larger social settings for normative healthy human development. This paper draws upon both Bronfenbrenner’s bioecological systems theory of human development and Ryan and Deci’s self-determination theory (SDT) to justify the importance of exposure to the kinds of experiences children normally receive in school settings for normative development. Theories related to virtual reality are also explored to evaluate the role that social presence, through robotic avatars, plays in providing homebound children with developmental experiences. This paper introduces the first systematic, multicase study on the robot-mediated presence of homebound children in traditional schools. Findings include empirical data that inform a theoretically supported framework for evaluating the robot-mediated presence of children in learning environments.
Maamuujav immigrated to the U.S. in 2002 after earning a B.A. in English and a M.A. in Linguistics from the University of the Humanities in Mongolia. She enrolled at CSULA, where she pursued a master’s degree in Teaching English to the Speakers of Other Languages at the Charter College of Education.
Upon completing her degree in 2005, Maamuujav began teaching in two departments at CSULA, while simultaneously obtaining a Master of Science in Public Administration. She enrolled in the UCI School of Education’s Ph.D. in Education program in 2018.
As a doctoral student at UCI, Maamuujav is working on two major research projects. Under the guidance of her advisor, Professor Emerita Carol Booth Olson, Maamuujav is a graduate researcher on a U.S. Department of Education grant to scale cognitive strategies instruction to writing and professional development in eight states (Arizona, California, Illinois, Minnesota, Nevada, Texas, Utah, and Wisconsin). Maamuujav’s portion of the research focuses on analyzing essay writing from linguistically and culturally diverse students.
“Undraa’s research on the syntactic and lexical features of adolescent English Learners’ text-based academic writing will make an important contribution to the field,” Olson said.
Under Associate Professor Penelope Collins, Maamuujav is serving as a co-researcher on a UCI Education Research Initiative to investigate the utility and effectiveness of infographics to scaffold undergraduate students’ writing skills development in process-based writing courses.
Maamuujav is sole author of a publication in CATESOL [California Teaching of English to Speakers of Other Languages] Journal: “Developing Autonomous Self-Editors: An Alternative Approach to Written Corrective Feedback”, and first author of an article in TESOL [Teaching of English to Speakers of Other Languages] Journal: “The utility of infographics in L2 writing classes: A practical strategy to scaffold writing development.”
She currently has four articles in press at peer-reviewed journals. This academic year, she will be presenting her research at two international conferences: TESOL International Convention and American Association of Applied Linguistics Conference.
The CDIP supports doctoral students whose goal is a tenure-track position at a California State University. Recipients receive financial support, mentorship by CSU faculty, and professional development and grant resources.
Professor is co-editor of new 3rd edition of Community Psychology: In pursuit of liberation and wellbeing
Community Psychology provides a thorough introduction to a variety of core concepts, theoretical frameworks, and different types of interventions, research, and social issues. The editors focus on three contemporary social issues to illustrate key concepts throughout the book: climate change, affordable housing and homelessness, and immigration.
The textbook is intended to develop students’ ability to think critically about the role of psychology in society.
The text is organized in four sections.
A companion website offers sample questions for each chapter in the text and prepared exercises for students that can be use in classrooms.
Yau is a postdoctoral scholar research associate at USC’s Department of Psychology. Her research interests include media psychology, developmental psychology, educational psychology, and child and adolescent development. For her doctoral work, she specialized in Human Development in Contest (HDiC).
Reich’s research foci include socio-emotional development, parent-child interactions, peer networks, and social affordances of technology. The bulk of her work explores direct and indirect influences on the child, specifically through the family, online, and school environment. She is a fellow of the American Psychological Association and the Society for Community Research and Action. At UCI, Reich is director of the Development in Social Context Lab (DISC) and serves as the associate director of the Ph.D. in Education program. She holds additional appointments in Psychological Science and Informatics.
Abstract of Chapter 2
Youth are increasingly using digital technology to connect with their peers. As such, there are overlaps between the people youth interact with face-to-face and those they interact with digitally. The emergence of social network sites, online gaming, social media apps (i.e., applications), and text messaging over the past two decades has evolved from separate areas where adolescents went to develop new relationships, try out new relationships, or maintain relationships with friends or family who live far away to a clear extension of their offline world. Nowadays, adolescents use digital platforms to develop and maintain relationships with friends and people they regularly interact with offline or with people who are only one or two degrees of separation from face-to-face friends (e.g., a friend of a friend). Although there are a few exceptions (e.g., online games), this connectivity between offline and digital spaces has led researchers to change how we conceptualize and study digital friendships over time. This chapter begins with a historical orientation of how youths’ friendships were characterized as offline and online and the ways in which the research has evolved to consider digital spaces as another setting where friends interact. We then explore new and potential directions for future research that focus on friendships that occur in the physical world as well as through a variety of digitally mediated spaces.
A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian filtering approaches, such as hidden Markov models and Kalman filters, and deep-learning approaches, such as the long short-term memory (LSTM) model, may be useful methods to estimate user skill level and predict appropriate task challenges. A possible advantage of these models over commonly used adaptive methods, such as staircases or blockwise adjustment methods that are based only upon recent performance, is that Bayesian filtering and deep learning approaches can model the trajectory of user performance across multiple sessions and incorporate data from multiple users to optimize local estimates. As a proof of concept, we fit data from two large cohorts of undergraduate students performing WM training using an N-back task. Results show that all three models predict appropriate challenges for different users. However, the hidden Markov models were most accurate in predicting participants' performances as a function of provided challenges, and thus, they placed participants at appropriate future challenges. These data provide good support for the potential of machine learning approaches as appropriate methods to personalize task performance to users in tasks that require adaptively determined challenges.
Santagata serves as the current director of UCI's Education Center for Research on Teacher Development and Professional Practice and as the School of Education's Global Engagement liaison. She has contributed as a visiting scholar, Institute of Advanced Studies, Alma Mater Studiorum, Bologna, Italy in 2019, and a visiting professor at the Technische Universität Berlin, Germany from 2018 to 2019.
Video has been used extensively in teacher preparation to develop noticing skills. Experienced teachers generally detect, understand and interpret the multiplicity of events that take place in the classroom, whereas novice teachers tend to focus their attention on more superficial aspects that are often not strictly relevant to students’ learning. This study presented video-recorded lessons both to a group of Italian novice teachers in training (without previous teaching experience) and to a group of more experienced teachers (with three or more years of service) with the aim of comparing the observation and interpretation skills of the two groups. Results confirmed prior findings: novices mostly described what they observed, focusing on the teacher’s actions and without demonstrating a critical stance nor suggesting instructional improvements. Contrary to prior research, the majority of novice participants did not focus on issues of or classroom climate or management, and differences between novice and more experienced teachers were not statistically significant. The discussion suggests various hypotheses that might explain these findings and highlights the need for professional development experiences that centre the work of teaching specifically on close analysis of practice and of student thinking.
“Interactive Dynamic Literacy Model: An Integrative Theoretical Framework for Reading-Writing Relations”
I propose an integrative theoretical framework for reading and writing acquisition, called the interactive dynamic literacy model, after reviewing theoretical models of reading and writing, and recent efforts in integrating theoretical models within reading and writing, respectively. The central idea of the interactive dynamic literacy model is that reading and writing are inter-related, developing together, largely due to a shared constellation of skills and knowledge. Four core hypotheses of the interactive dynamic literacy model include (1) hierarchical structure of component skills with direct and indirect relations; (2) interactive relations between component skills, and between reading and writing; (3) co-morbidity of reading and writing difficulties; and (4) dynamic relations (relations change as a function of development, learner characteristics, and reading and writing measurement). Implications and future work are discussed.
“Understanding proficiency: Analyzing the characteristics of secondary students’ on-demand analytical essay writing”
Fifth-year doctoral student Vicky Chen (left), Professor Emerita Carol B. Olson (center), and UCI Writing Project Director of Research Huy Quoc Chung (right) have published in the Journal of Writing Assessment. The title of their article is “Understanding proficiency: Analyzing the characteristics of secondary students’ on-demand analytical essay writing.”
Chen, who is advised by by Professor Emerita Carol Booth Olson, is specializing in Language, Literacy, and Technology. Her research interests include academic writing, extracurricular writing, writing instruction, critical reading and analysis, language acquisition, and students and teachers with disabilities.
Olson directs both the WRITE Center and the UCI/National Writing Project. She co-founded the UCI Writing Project in 1978 and served as director until 2019. Olson has been awarded more than $50 million in research grants to advance the teaching of reading and writing. She authored several books, including The Reading/Writing Connection, and published over 30 journal articles on interactive strategies for teaching writing, fostering critical thinking through writing, applying multiple intelligences theory to language arts instruction, using multicultural literature with students of culturally diverse backgrounds.
As Writing Project Director of Research, Chung oversees the research efforts of the Pathway Project’s OELA and i3 grants. Since completing his doctoral studies specialized in Learning, Cognition, and Development and Language, Literacy, and Technology, he has managed and worked on three federally funded programs regarding formative assessments in mathematics, testing accommodations for English Learners, and an evaluation of the Writing Reform and Innovation for Teaching Excellence (WRITE) professional development program. Chung’s research foci follow his commitment to teacher education and professional development for English Language Arts teachers.
This study investigated the different characteristics of not-pass (n = 174), adequate-pass (n = 173), and strong-pass (n = 114) text-based, analytical essays written by middle and high school students. Essays were drawn from the 2015-2016 Pathway writing and reading intervention pretests and posttests. Results revealed the use of relevant summary was an important difference between not-pass and adequate-pass essays where significantly more adequate-pass essays used summary in a purposeful rather than general way. In contrast, major characteristics that set apart strong-pass essays from adequate-pass essays involved providing analysis and including a clear conclusion or end. Factors that affected these characteristics such as whether the writer made claims and comments about the text are discussed, and some instructional strategies are suggested.