CEECS post docs L-R: Helena Mawdsley, Tia N. Barnes, Tiffany McMonigle, Feihong Wang, Crystal Crowe Bishop, and Salih Rakap

The UF Office of Postdoctoral Affairs will hold its second annual Posdoctoral Research Symposium on Monday, April 21, 2014 at Emerson Alumni Hall from 8am – 3pm. The theme for this year’s symposium is Communicating Your Research. Postdocs in both oral and poster presentations will be judged on how well they can convey their research to an educated lay audience. Communication Skills is one of the six Core Competencies the National Postdoctoral Association promotes as critical to postdoc success.

Postdoctoral Fellows in the COE Center for Excellence in Early Childhood Studies (CEECS) have completed the following research:

Characterizing Transition Practice Intensity in Early Childhood
Helena Mawdsley, Patricia Snyder, and Beth Rous

This poster presents findings from a systemic review of the literature on early childhood transition practices. The review characterizes practices described in the transition literature with respect to “who,” “when,” “what,” and “how” to explicate further which intensity of transition practices are used with which children and families and under what circumstances. Article criteria were (a) include children from birth through age 5 years; (b) include transition practices data either from national surveys or from empirical studies transition practices. Data bases searched: EBSCO, PsychInfo, ERIC, and Medline.  Articles were screened to verify they meet inclusion criteria. The final set of articles were coded using investigator-defined variables and associated coding categories that permit characterizations of the intensity of the practices and the “who,” “when,” “what,” and “how” of early childhood (EC) transition practices. Variables and coding categories associated with the “who” category characterized the type of children (i.e., with a disability or without disability) with whom the transition practices were used. The “when” variables and coding categories characterized the transition time point. The “what” variables and coding categories characterized the transition practices used and their intensity. The “how” variables and coding categories characterized the strategies used to implement the practice. Descriptive statistics for each coding variable were generated. A majority of articles included children with disabilities. Preschool into kindergarten was the most frequently studied time point. Five high intensity practices with 11 corresponding strategies and 3 low intensity practices with 9 corresponding strategies appeared to be most common among the articles.

The Influence of Student Demographics on CBI Effectiveness
Tia N. Barnes, Stephen Smith, and Ann Daunic

Problem behavior can have a negative effect on the overall school environment. Among the possible causes of problem behaviors are cognitive processing deficits and distortions that can be addressed by school personnel through the use of cognitive behavioral interventions (CBI).  In recent years, CBI research has moved from the determination of efficacy to extension and refinement of CBIs to better meet the needs of a diverse school population.  There are limitations in the current school-based CBI literature including a lack of focus on the effectiveness of CBI for culturally diverse and low income students.  This study addressed this limitation by examining whether student socio-economic status (SES) and ethnicity were associated with how Tools for Getting Along (TFGA), a universally delivered CBI for 4th and 5th grades, affected socio-emotional outcomes. Using extant data, we conducted mediation and moderation analyses using structural equation modeling (SEM; Preacher et al., 2011) to investigate the effects of demographic variables on the efficacy of TFGA for increasing positive social problem solving and reducing negative social problem solving, externalizing behavior, and aggression. The analysis revealed that TFGA participants qualifying for low SES had more externalizing behavior (β = -0.076, p < .05) but less reactive aggression (β = 0.069, p < .05) than control participants. Student race did not moderate the effect of TFGA on the outcome variables of interest. This presentation will include a discussion of possible intervention refinements to improve CBI effectiveness with certain student populations and propose future research directions.

Preschool Teacher-Child Relationships: Influences on Social and Behavioral Skills of Young Children with or At-risk for Emotional/Behavioral Disabilities
Tiffany McMonigle and Patricia Snyder

When children form positive relationships with teachers, they are more likely to self- regulate their behavior and have meaningful social interactions with others (Williford, Whittaker, Vitiello, & Downer, 2013). Children are more apt to view school in a positive way and establish friendships, when they have a close relationship with their teacher (Hamre & Pianta, 2006). Most studies examining teacher-child relationships have been conducted with children who are typically developing (Eisenhower, et al., 2007). The purpose of this descriptive systematic review was to examine the empirical research regarding how preschool teacher-child relationships affect social skills and challenging behavior for young children with or at-risk for emotional/behavioral disabilities. Restricting the search to peer-reviewed journal publications in English, six databases were searched using the following search string: “At Risk” AND Preschoo* OR Early Child* AND Teacher-child Relationships AND Social Skills OR Intervention.* In all 6,039 publications were identified for screening. Both a primary and secondary screener identified articles that met a more in depth inclusion criteria. A total of six sources were identified for coding. Coding categories and operational definitions for each coding category were refined within the coding protocol and agreed upon by the authors. All identified sources were double coded by the secondary coder. Across the six sources, preschool teacher-child relationships had positive associations with behavioral adjustment, emotion knowledge, peer interactions, and frustration tolerance. The limited number of sources found, included homogenous risk factors suggesting the need for continued research incorporating additional risk factors.

Using Qualitative Rating Methodology and Latent Class Analysis to Identify Young Rural Children with Vulnerabilities and Strengths
Feihong Wang, James Algina, Patricia Snyder, University of Florida; Martha Cox, University of North Carolina at Chapel Hill; The Family Life Project Key Investigators

Individual differences in attention, interests, persistence, and mastery pleasure have significant implications for school readiness and success (e.g., Wigfield, Eccles, Schiefele, Roeser, & Davis-Kean, 2006). A person-oriented approach to analyzing child variables may help identify categories of behavior patterns in children that may be amenable to prevention or interventions (Chazan-Cohen, Halle, Barton, & Winsler, 2012).  We first examined patterns of children’s affect and behavior (N=1125) during challenging problem solving puzzle task at 24 and 35 months. We next examined children’s membership shifts in different patterns from 24 to 35 months in order to identify children who may be vulnerable or resilient for school challenges. Latent class analysis for 2- to 5-class models was conducted using 6 child codes to explore patterns of children’s affect and behavior at both 24 and 35 months. AIC, BIC, entropy indexes and interpretability of classes were compared across models to select optimal and parsimonious models. Finally, a latent transition analysis was conducted to examine shifts in children’s membership in different patterns over time. We found four consistent patterns of child affect and behavior at 24 and 35 months: a positive-motivated pattern, a negative-disengaged pattern, a content-compliant and an emotional-mixed pattern. There were also substantial shifts in children’s memberships in the different patterns from 24 to 35 months. These findings suggest there are different levels of strengths and vulnerabilities in children’s task oriented behaviors. Early intervention services may need to tailor their approaches to support strengths and address vulnerabilities in young children.

Exploring Measurement Invariance for the ECERS-R and the Arnett Caregiver Interaction Scale: Findings from the Early Childhood Longitudinal Study, Birth Cohort
Crystal Crowe Bishop, Patricia A. Snyder, and James Algina

Quality in early care and education (ECE) is a national priority. The Early Childhood Environment Rating Scale-Revised (ECERS-R; Harms, Clifford, & Cryer, 1998, 2005) and the Arnett Caregiver Interaction Scale (CIS; Arnett, 1989) are two instruments used widely to characterize different dimensions of quality in ECE. Although studies have been conducted to gather validity evidence about these instruments (Colwell et al., 2012; Gordon et al., 2013), there is limited evidence regarding whether scores from these instruments ECE are valid for making inferences about the quality of ECE provided across different types of ECE classrooms. The purpose of this study was to determine the extent to which scores from the ECERS-R and the CIS were comparable across preschool classrooms in which children with special needs were enrolled (i.e., inclusive preschool classrooms) and preschool classrooms in which no children with special needs were enrolled. This study involved secondary analyses of cross-sectional data from the Early Childhood Longitudinal Study—Birth Cohort. Multiple group confirmatory factor analyses (Jöreskog, 1971) were conducted to examine whether each instrument measured the same latent variables and the extent to which scores from each instrument were comparable across the two types of classrooms observed. Findings from this study suggested strong evidence of measurement invariance for both instruments across the two types of classroom studied, providing preliminary evidence to suggest scores from each instrument can be used to make inferences about the quality of ECE provided in both types of classroom.

Evaluating Treatment Effect in Single-Subject Experimental Research: A Comparison of Five Nonoverlap Methods
Salih Rakap and Patricia Snyder

Visual analysis has been the primary method for evaluating treatment effect in single-subject experimental research (SSER). Several organizations and funding agencies have suggested use of quantitative methods (e.g., nonparametric nonoverlap methods) as additional result interpretation aides for evaluating treatment effect in SSER. Discussions about how to analyze data obtained from SSER continues. In this study, five nonoverlap methods were compared using 222 A-B graphs obtained from 36 studies of naturalistic instructional approaches. Results showed that IRD and Tau-U were superior to other methods in discriminability and agreement with visual analysts’ judgments. Presenters discuss implications for research and practice.

LearnLab, an NSF Science of Learning Center, will hold its third annual Learning Science Workshop Research and Innovation for Enhancing Achievement and Equity on June 14 – 15, 2014 at Carnegie Mellon University, Pittsburgh PA. Applications are due May 15, 2014. The workshop is targeted to senior graduate students, post-docs, and early career faculty. This free professional development workshop will include keynote speakers Dr. Tawanna Dillahunt and Dr. Charles Isbell.

Applicants should email their CV, this demographic form, a proposed presentation title and abstract, and a brief statement describing their research interests to Jo Bodnar (jobodnar@cs.cmu.edu) by May 15, 2014. Please use the subject Application for LearnLab Summer Workshop 2014. Upon acceptance, you will be notified if you have been selected for a talk or poster presentation.

There is no registration fee for this workshop.  However, attendance is limited so early applications are encouraged.  Scholarships for travel are available.  Scholarships will be awarded based on your application, including your research interests, future plans, and optional recommendation letter.

For more information, contact Michael Bett, LearnLab Managing Director, at (412) 268-8616 or mbett@cs.cmu.edu

Important Dates

• May 15 Application Deadline
• May 29 Notification of Acceptance
• June 14-15 Workshop held at Carnegie Mellon University in Pittsburgh

This exciting summer research opportunity is available to early career researchers in the fields of psychology, education, computer science, human-computer interfaces, and language technologies. The workshop seeks broad participation, especially by members of underrepresented groups who may be considering a research or faculty position in the learning sciences.

This year’s workshop Research and Innovation for Enhancing Achievement and Equity will include five areas:

• Enhancing Achievement through Educational Technology and Data Mining.  Using domain modeling, and large datasets to discover when learning occurs and to provide scaffolding for struggling students. See www.learnlab.org/research/wiki/index.php/Computational_Modeling_and_Data_Mining.
• 21st Century Skills, Dispositions, and Opportunities. Re-examining the goals of education and assessment and considering transformative changes in how and where learning occurs.
• Opening Classroom Discourse.  Studying how classroom talk contributes to domain learning and supports equity of learning opportunity.  See LearnLab’s Social-Communicative Factors thrust www.learnlab.org/research/wiki/index.php/Social_and_Communicative_Factors_in_Learning.
• Course-Situated Research.  Running principle-testing experiments while navigating the complex waters of real-world classrooms.  See www.learnlab.org/research/wiki/index.php/In_vivo_experiment.
• Motivation Interventions for Learning. Implementing theory based motivational interventions to target at risk populations to improve robust student learning. See http://www.learnlab.org/research/wiki/index.php/Metacognition_and_Motivation

The substantive focus of the workshop is the use of current research and innovations to enhance achievement and equity at all levels of learning. Activities will include demonstrations of the diverse set of ongoing learning sciences research projects at LearnLab, and poster presentations or talks by participants. Participants will also meet with LearnLab faculty in research groups and various informal settings. Information will be provided about becoming a part of the Carnegie Mellon or University of Pittsburgh learning science community.

In addition to these substantive themes, the workshop will provide participants with opportunities for professional development and the chance to gain a better understanding of the academic career ladder. These include mentoring that focuses on skills, strategies and “insider information” for career paths. Sessions will include keynote speakers and LearnLab senior faculty discussing professional development topics of interest to the attendees. These may include the tenure and promotion process, launching a research program, professionalism, proposal writing, among other topics.

The workshop will have two distinguished keynote speakers:

Dr. Tawanna Dillahunt is a Presidential Postdoctoral Fellow at the University of Michigan’s School of Information. Her research interests are in the areas of human-computer interaction, ubiquitous computing, and social computing. She is primarily interested in identifying needs and opportunities to further explore how theories from the social sciences can be used to design technologies that have a positive impact on group and individual behavior. With the narrowing of the digital divide, the ubiquity of smart devices and mobile hotspots in common places in the U.S. (e.g., libraries, community centers, and even McDonald’s) she sees an urgent need to explore the use of these technologies for those that stand the most to gain from these resources. Therefore, her research targets the use of these technologies among people in disadvantaged communities. Results from her past studies in the environmental sustainability domain suggest that improved communication provides individual community members with access to new information and helps to resolve common problems. Dr. Dillahunt plans to continue to apply her past research techniques to clarify and potentially meet the needs of disadvantaged, and often understudied communities in environmental and economic sustainability, and in other domains such as education and health. Her goal is to design and enhance innovative technologies to solve real-world problems.

She holds a M.S. and Ph.D. in Human-Computer Interaction from Carnegie Mellon University, a M.S. in Computer Science from the Oregon Graduate Institute School of Science and Engineering (now a part of the Oregon Health and Science University in Portland, OR), and a B.S. in Computer Engineering from North Carolina State University. She was also a software engineer at Intel Corporation for several years.

Dr. Charles Isbell is a Senior Associate Dean and Professor in the School of Interactive Computing in the College of Computing at Georgia Institute of Technology. Dr. Isbell’s research passion is artificial intelligence. In particular, he focuses on applying statistical machine learning to building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human.

Lately, Dr. Isbell has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition); scalable coordination; and development environments that support the rapid prototyping of adaptive agents. As a result he has begun developing adaptive programming languages, worrying about issues of software engineering, and trying to understand what it means to bring machine learning tools to non-expert authors, designers, and developers.

Dr. Isbell earned his M.S. and Ph.D. from MIT and his B.S. in Computer Science from Georgia Tech in 1990.