An exploration of parent ivolvement in Response to Intervention (RTI) in Title I schools

Research demonstrates that parental involvement positively impacts student achievement and enhances targeted instruction. Notably, however, little research currently exists on how schools involve parents in Response to Intervention (RTI), a framework for implementing targeted, tiered, research-based instruction. The purpose of this study was to interview selected parents, teachers, RTI specialists, and principals in three Title I elementary schools in one school district, plus one district-level administrator, in order to examine how elementary schools currently involve parents in RTI prereferral interventions, and to understand the factors that might facilitate or challenge such parent involvement. I employed a comparative case study qualitative design with each elementary school as the main unit of analysis. I conducted individual, in-depth interviews that lasted approximately 45-60 minutes with a total of 33 participants across the three school sites, including 11 parents, 12 teachers, and six RTI specialists, three principals, and one district-level administrator. I also analyzed documents related to RTI processes that are available through websites and participants. I used Strauss and Corbin’s (1998) three-step scheme for thematic/grounded theory analysis, and Atlas.ti as the electronic tool for management and analysis. Analyses of the data revealed that personnel across the sites largely agreed on how they explain RTI to parents and notify parents of student progress. Parents mostly disagreed with these accounts, stating instead that they learn about RTI and their child’s progress by approaching teachers or their own children with questions, or by examining report cards and student work that comes home. Personnel and parents cited various challenges for involving parents in RTI. However, they all also agreed that teachers are accessible and willing to reach out to parents, and that teachers already face considerable workloads. It appears that no district- or school-wide plan guides parent involvement practices in RTI at any of the three schools. Finally, I present a discussion of findings; implications for teachers, RTI implementation leaders, and Title school leaders; study limitations; and possibilities for future research.

Connecting the dots: Examining the role of parental beliefs and preschoolers’ affect and engagement in predicting parent-child number exploration during a meaningful math experience

The current study examined the frequency and quality of how 3- to 4-year-old children and their parents explore the relations between symbolic and non-symbolic quantities in the context of a playful math experience, as well as the role of both parent and child factors in this exploration. Preschool children’s numerical knowledge was assessed while parents completed a survey about the number-related experiences they share with their children at home, and their math-related beliefs. Parent-child dyads were then videotaped playing a modified version of the card game War. Results suggest that parents and children explored quantity explicitly on only half of the cards and card pairs played, and dyads of young children and those with lower number knowledge tended to be most explicit in their quantity exploration. Dyads with older children, on the other hand, often completed their turns without discussing the numbers at all, likely because they were knowledgeable enough about numbers that they could move through the game with ease. However, when dyads did explore the quantities explicitly, they focused on identifying numbers symbolically, used non-symbolic card information interchangeably with symbolic information to make the quantity comparison judgments, and in some instances, emphasized the connection between the symbolic and non-symbolic number representations on the cards. Parents reported that math experiences such as card game play and quantity comparison occurred relatively infrequently at home compared to activities geared towards more foundational practice of number, such as counting out loud and naming numbers. However, parental beliefs were important in predicting both the frequency of at-home math engagement as well as the quality of these experiences. In particular, parents’ specific beliefs about their children’s abilities and interests were associated with the frequency of home math activities, while parents’ math-related ability beliefs and values along with children’s engagement in the card game were associated with the quality of dyads’ number exploration during the card game. Taken together, these findings suggest that card games can be an engaging context for parent-preschooler exploration of numbers in multiple representations, and suggests that parents’ beliefs and children’s level of engagement are important predictors of this exploration.

READY FOR KINDERGARTEN: A TRAINING PROGRAM DESIGNED TO ENCOURAGE PARENT-CHILD CONVERSATION DURING THE PRESCHOOL YEARS

Many children in the United States begin kindergarten unprepared to converse in the academic language surrounding instruction, putting them at greater risk for later language and reading difficulties. Importantly, correlational research has shown there are certain experiences prior to kindergarten that foster the oral language skills needed to understand and produce academic language. The focus of this dissertation was on increasing one of these experiences: parent-child conversations about abstract and non-present concepts, known as decontextualized language (DL). Decontextualized language involves talking about non-present concepts such as events that happened in the past or future, or abstract discussions such as providing explanations or defining unknown words. As caregivers’ decontextualized language input to children aged three to five is consistently correlated with kindergarten oral language skills and later reading achievement, it is surprising no experimental research has been done to establish this relation causally. The study described in this dissertation filled this literature gap by designing, implementing, and evaluating a decontextualized language training program for parents of 4-year-old children (N=30). After obtaining an initial measure of decontextualized language, parents were randomly assigned to a control condition or training condition, the latter of which educated parents about decontextualized language and why it is important. All parents then audio-recorded four mealtime conversations over the next month, which were transcribed and reliably coded for decontextualized language. Results indicated that trained parents boosted their DL from roughly 17 percent of their total utterances at baseline to approximately 50 percent by the mid-point of the study, and remained at these boosted levels throughout the duration of the study. Children’s DL was also boosted by similar margins, but no improvement in children’s oral language skills was observed, measured prior to, and one month following training. Further, exploratory analyses pointed to parents’ initial use of DL and their theories of the malleability of intelligence (i.e., growth mindsets) as moderators of training gains. Altogether, these findings are a first step in establishing DL as a viable strategy for giving children the oral language skills needed to begin kindergarten ready to succeed in the classroom.

Adapting Behavioral Parent Training as an Interactive Computer Game

Behavioral Parent Training (BPT) is a well-established therapy that reduces child externalized behaviors and parent stress. Although BPT was originally developed for parents of children with defiant behaviors, the program’s key concepts are relevant to parenting all children. Since parents might not fully utilize BPT due to cost and program location, we created an online game as a low-cost, easily accessible alternative or complement to BPT. We tested the game with nineteen undergraduate students at the University of Maryland. The experimental group completed pretest survey on core BPT knowledge, played the game, and completed a BPT posttest, while the control group completed a pretest and posttest survey over a three week period. Participants in the experimental group also completed a survey to indicate their satisfaction with the overall program. The experimental group demonstrated significantly higher levels of BPT knowledge than the control group and high levels of satisfaction. This suggests that an interactive, online BPT platform is an engaging and accessible way for parents to learn key concepts.

Childhood Attention Problems and the Development of Comorbid Symptoms at the Transition to High School: The Mediating Role of Parent and Peer Relationships

Children with Attention-Deficit/Hyperactivity Disorder (ADHD) are at increased risk for the development of depression and delinquent behavior. Children and adolescents with ADHD also experience difficulty creating/maintaining high quality friendships and parent-child relationships, and these difficulties may contribute to the development of co-morbid internalizing and externalizing symptoms in adolescence. However, there is limited research examining whether high quality friendships and parent-child relationships mediate the relation between ADHD and the emergence of these co-morbid symptoms at the transition to high school. This study examines the mediating role of relationship quality in the association between ADHD and depressive symptoms/delinquent behaviors at this developmentally significant transition point. Results revealed significant indirect effects of grade 6 attention problems on grade 9 depressive symptoms through friendship quality and quality of the mother-child relationship in grade 8. Interventions targeting parent and peer relationships may be valuable for youth with ADHD to promote successful transitions to high school.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.