Data-based Decision Research Articles

Intensifying Instruction to Meet Students’ Early Writing Needs

With over 70% of fourth-, eighth-, and twelfth-grade students in the United States scoring below proficiency levels in writing (NCES, 2009), teachers must provide early and effective writing intervention to accelerate students’ writing skills to meet grade-level standards. In this paper, we provide teachers with a theoretical framework to conceptualize the writing process, link evidenced-based writing interventions to each component of writing, and discuss how to create a writing instructional plan to individualize instruction. Then, we outline a data-based decision making process for intervention delivery. Our aim is to provide educators with steps to identify students’ writing needs, translate these needs into actionable teaching strategies, and accelerate writing progress for students who struggle to meet grade-level writing standards.

ToxAnalyzer: A user-friendly web tool for interactive data analysis and visualization of chemical compounds from the Comparative Toxicogenomics Database (CTD)™

Toxicology is a field ofscience that has undergone changes in recent years. Using modern sequencing technologies, it went from an exclusive animal-based science to a field of data-based decision making. Many databases store important data about toxicology experiments, which can be useful to understand the mechanisms concerning chemical product toxicity. One of these databases is the “Comparative Toxicogenomics Database” (CTD) that provides information about diseases, genes, compounds, and their role in toxicity. However, a simple search in CTD produces a high volume of data to be manually and individually analyzed. Most tools available to analyze CTD’s data require previous installation and the knowledge of writing and manipulating programming scripts. Here, we show ToxAnalyzer, a web tool that provides an organized overview of the complete compound data available in CTD to aid users in possible toxicogenomic studies. Our application withdraws data from CTD’s servers and uses a set of Python scripts to process and display important information about any chemical compound found in CTD. Using the chemical name, a user can gather information about related publications, associated organisms, gene interactions, and other relative plots. If users need more information about an interaction between a specific gene and a chemical compound, PubMed ID links are available to access the original publications. Therefore, ToxAnalyzer is user-friendly and can be accessed with any device with internet access, requiring absolutely no programming knowledge. ToxAnalyzer is available at https://tox-analyzer.com/.

Training Preservice Practitioners to Make Data-Based Instructional Decisions

Adapting interventions based on learner progress is paramount to the effectiveness of interventions in special education and applied behavior analysis. Although there is some research on effective methods for training practitioners to make general instructional decisions (e.g., modify an intervention) based on graphed performance data, research on training individuals to make specific decisions (e.g., how to modify an intervention) is more limited. Our purpose in this study was to evaluate the effects of a training package, consisting of a brief online training and a visual decision-making model, for increasing preservice teachers’ and behavior analysts’ accuracy in making specific instructional decisions based on graphed performance data. In a multiple baseline across participants design, all participants increased their decision-making accuracy on novel graphs during assessment sessions and maintained accuracy at 1-month follow-up. The implications of these findings for training and future research on data-based decision-making are discussed.

Graph Construction and Visual Analysis: A Comparison of Curriculum-based Measurement Vendors

Curriculum-based measurement (CBM) represents a critical strategy for data-based decisionmaking within educational settings. Visual analysis is frequently used to analyze CBM data; thus, CBM vendors often automatically generate graphs based on student data to facilitate analysis. Differences in graph formatting are apparent across CBM vendors, and currently, the extent to which these differences may influence rater interpretation of data is unknown. As such, the current study sought to evaluate whether there were differences in rater evaluation of presence and magnitude of an intervention effect when identical data were plotted on graphs of four CBM vendors: AIMSweb, FastBridge Learning, easyCBM, and DIBELS Next. Results of the study indicated that visual analysts rated. Results of the study found that probability of identifying the presence of a treatment effect was similar across vendors. However, differences emerged with respect to ratings of magnitude of effect, with raters indicating the greatest magnitude of intervention effect for easyCBM and DIBELS Next graphs. As data graphs generated by CBM vendors may be utilized by school personnel to make decisions regarding the continuation, discontinuation, or alteration of intervention procedures, the results of the study indicate the need for increased consideration in the manner in which data are presented visually such that accurate decisions are made regarding student progress and response to intervention.

RETRACTED: User acceptance of machine learning models – Integrating several important external variables with technology acceptance model

Machine learning models enable data-based decision-making in many areas and have attracted extensive attention. By testing the factors that influence the adoption of machine learning models, this study expands the scope of machine learning models in information technology adoption research. Based on the machine learning background and Technology Acceptance Model, this study integrates the necessary external variables, proposes a research model, and further verifies the validity of the model through the survey of 192 users of machine learning models. The results showed that organizational factors, trust, perceived usefulness, and perceived ease of use are positively correlated with the attitude of machine learning models. Moreover, our findings show that the interpretability of the model has an important positive effect on trust. The factors examined in this study are the basis for the development and use of reliable machine learning models. And it has important practical significance for promoting user adoption of machine learning model. Meanwhile, these theoretical studies also provide a strong literature support for the adoption of machine learning models and fill the theoretical research gap in this field.

Understanding How Language of Instruction Impacts Early Literacy Growth for Spanish-Speaking Children

Multitiered systems of support hold promise for dual language learners when culturally and linguistically responsive practices guide instruction. We modeled growth on Spanish and English early literacy skills and examined the role of language exposure and use at the individual and classroom instruction level in a group of 313 Spanish-English-speaking bilingual preschool-age children from 81 classrooms. Results revealed a significant portion of variance in children’s performance was between classrooms. Children demonstrated meaningful growth on all measures except English rhyming. Predominantly Spanish and bilingual instruction produced growth as strong as English instruction on all measures except for first sounds and sound identification where bilingual instruction had a negative impact on growth. Children’s language profiles did not interact with their classroom language of instruction. Implications for understanding the role language of instruction and home language exposure in multitiered systems of support with Spanish-speaking preschoolers are discussed. Impact Statement Preschool-age Spanish-speaking dual language learners grow as quickly in Spanish language instruction as in English instruction on Spanish and English alphabet knowledge and phonological awareness skills. Preschool-age Spanish-speaking dual language learners did not grow as quickly in settings self-identified as providing bilingual instruction as in English-only instruction on English alphabet knowledge and phonological awareness skills. More research is needed to identify the factors in these settings that are related to this difference. Early childhood MTSS models can improve young DLL’s outcomes by facilitating data-based decisions that unpack the nuances of their performance. It is important when considering the needs of DLLs to include performance in each of their languages, understand expected growth rates in each of their languages, and consider tier 1 language of instruction.

A Systematic Review of CBM Professional Development Materials: Are Teachers Receiving Sufficient Instruction in Data-Based Decision-Making?

The purpose of this study was to examine the amount of attention devoted to data-based decision-making in Curriculum-Based Measurement (CBM) professional development materials. Sixty-nine CBM instructional sources were reviewed, including 45 presentations, 22 manuals, and two books. The content of the presentations and manuals/books was coded into one of four categories: (a) general CBM information, (b) conducting CBM, (c) data-based decision-making, and (d) other. Results revealed that only a small proportion of information in the CBM instructional materials was devoted to data-based decision-making (12% for presentations and 14% for manuals/books), and that this proportion was significantly smaller than (a) that devoted to other instructional topics, (b) that expected were information to be equally distributed across major instructional topics, and (c) that recommended by experienced CBM trainers. Results suggest a need for increased attention to data-based decision-making in CBM professional development.

Using Evidence-Based Practice and Data-Based Decision Making in Inclusive Education

There are longstanding calls for inclusive education for all regardless of student need or teacher capacity to meet those needs. Unfortunately, there are little empirical data to support full inclusion for all students and even less information on the role of data-based decision making in inclusive education specifically, even though there is extensive research on the effectiveness of data-based decision making. In this article, we reviewed what data-based decision making is and its role in education, the current state of evidence related to inclusive education, and how data-based decision making can be used to support decisions for students with reading disabilities and those with intellectual disabilities transitioning to adulthood. What is known about evidence-based practices in supporting reading and transition are reviewed in relationship to the realities of implementing these practices in inclusive education settings. Finally, implications for using data-based decisions in inclusive settings are discussed.

Back to basics? A critical reflection on the use of frequency-building and Precision Teaching to support pupil intervention in foundational curricular learning

Precision Teaching (PT) presents as a systematic educational approach used to monitor and evaluate pupil performance, as underpinned by data-based decision making. As part of the implementation process, PT emphasises the importance of frequency-building to promote behavioural fluency for the learner. This article seeks to critically examine the potential application of frequency-building and PT in Irish mainstream primary schools to support pupil intervention in foundational curricular learning. Particular focus is placed on core academic subjects, in light of the national governmental drive to increase literacy and numeracy standards for all. This article provides an overview of the PT approach to intervention, with reference to recent Irish developments in the field spanning research and practice. The application of frequency-building and PT within a special education context is considered, particularly for pupils struggling to move beyond the ‘acquisition’ stage of the learning hierarchy. The author critically considers the alignment of PT with key tenets of the guidelines from the Department of Education and Skills (2017a) for supporting pupils with special educational needs in mainstream schools. Finally, limitations of PT-related research are forwarded, with reference to applied research in the field. Based on cumulative findings, the author advocates for a balanced and contextualised approach to fluency-focused instruction within schools, in light of the learning hierarchy and alternate, non-behaviourist approaches to teaching and learning.

Educational Leaders’ Perceptions and Uses of School Climate Data

National interest in using school climate as an accountability measure makes it important to understand how school leaders view and make use of school climate data. The purpose of this study was to investigate how school and district administrators use climate data in Virginia, where a statewide school climate survey is annually administered. School principals ( N = 283) completed surveys concerning their use of the school climate results for their school. Simultaneously, semi-structured interviews were conducted with school division leaders and school administrators ( N = 10) to deepen our understanding of how the school climate results were being used. Qualitative content analysis was used to identify patterns in the data. Study results reflect a consensus by educational leaders on the utility of receiving school climate data in order to make data-based decisions to improve outcomes for students. Participants provided thoughtful and constructive feedback on the importance of student relationships, perceptions of student subgroups, comparing their data with other schools, and ways to facilitate data-based decision-making. Participants also expressed a desire for additional support in understanding statistical results and generating recommendations for school improvement. These findings suggest that increasing the clarity and comprehensiveness of school climate data reports and providing support for interpreting the results will increase educational leaders’ ability to use school climate surveys.

Evaluating Augmentative and Alternative Communication Modalities for Individuals With Developmental Disabilities: a Brief Review of the Last 5 Years

This review is to extend our analysis of comparative AAC modality studies by evaluating the literature within the last 5 years as related to individuals with developmental disabilities. The findings are consistent with previous reviews of this topic and indicate that a variety of AAC modalities may be effective for individuals with developmental disabilities. Furthermore, these findings highlight the need to assess learner prerequisite skills and learner preference over time. This review confirms the need for assessments across a variety of variables and data-based decision making when considering AAC modalities for individuals with developmental disabilities.

Strategi Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatera Utara dalam Peningkatan Mutu Perguruan Tinggi Swasta

<p><em><em>This study aims to analyze internal and external factors and formulate strategies in improving the quality of higher education institutions in LLDIKTI Region I, North Sumatra. This research uses descriptive quantitative research methods. The analysis method used is a SWOT analysis using an analysis of internal factors and external factors. Strategies that can be carried out by LLDIKTI Region I North Sumatra are improvement and development of data-based information systems in coaching the quality of private universities, internal strengthening of LLDIKTI Region I North Sumatra in terms of more competent human resources, implementing and developing data-based decision support systems that can map quality PTS, guidance in implementing SPMI, provides training to increase the number of qualified auditors.</em></em></p>

A systematic literature review on developing students’ statistical literacy skills

Statistical literacy is crucial for preparing students to become part of a modern society that is continuously bombarded with arguments that are derived from data-based decisions. In this regard, the teaching of statistics is a fundamental aspect of the mathematics curriculum at any educational level. Therefore, this study presents a systematic literature review of 36 articles that examine statistical literacy skills among students. Specifically, this study focused on identifying teaching methods that influence the development of statistical literacy among students. It was found that four dominant factors influence the development of statistical literacy among students: the learning environment, students’ attitude, teaching method, and students’ basic knowledge. Some studies have focused on students’ demographic factors, including their age, gender, and grade level; however, there are no conclusive findings on their role in influencing the development of statistical literacy. The study also revealed the three most common teaching methods in statistical literacy studies were teacher-centered teaching methods, student-centered teaching methods, and materials-based teaching. Out of these methods, materials-based teaching was the most commonly used to develop statistical literacy among students. Based on the findings, implications for teaching and learning statistics were discussed.

Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker

The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. Then, we analyze the decision-making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision-making and that the amplitude distribution of the original laser chaos time series is not optimal. Hence, we introduce a new parameter for adjusting the amplitude distribution of the laser chaos to enhance the decision-making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.

How does information consistency influence prospective teachers’ decisions about task difficulty assignments? A within-subject experiment to explain data-based decision-making in heterogeneous classes

In order to provide adaptive instruction in heterogeneous classes, teachers have to consider performance-related information for their decisions on which task difficulty level fits best for a particular student. Focusing on prospective teachers’ data-based decision-making, we aim to investigate their use of proximal and distal indicators of students’ ability and readiness to deal successfully with domain-specific tasks that are available for 32 student cases in a digital test environment. To address several hypotheses on the participants’ information-processing behaviors and decisions, we conducted a within-subject experiment in which we systematically varied the informational consistency of the presented student cases. We used a mixed-methods approach to measure observable information-processing behavior and decision-making. We assessed the effects of consistent/inconsistent cases on the amount, sequence, and perceived relevance of selected information, the type of processed information, the time needed for information processing, and the subjective confidence when making a decision via linear dynamic panel-data modeling.

Educator Perspectives on Data-Based Decision Making in Germany and the United States

The purpose of this explorative study was to examine the use and understanding of key components of data-based decision making by educators in two countries—Germany and the United States. Educators responded to a survey that asked about data use and characteristics related to data-based decision making (DBDM). Results suggest educators in both countries are focused on using data to monitor progress, although less so in Germany. Educators in both countries noted similar understanding of important features (e.g., psychometric properties) of data. Educators in the United States reported they used data for decision making at the classroom level almost twice as often as their counterparts in Germany, while German educators focused on decision making at the student level. These findings will influence future research, including joint studies that could use the best practices of both countries, and professional learning opportunities for educators in Germany and the United States.

Incorporating big data within retail organizations: A case study approach

The rapid growth in consumer-generated Big Data that are mostly sourced from various types of mobile devices and sensor technologies has placed increasing competitive pressure on retailers to leverage such data within their location decision-making practices. This paper examines the incorporation of Big Data within retail organizations. Through the analysis of three in-depth case-studies of major retail-related organizations operating in Canada, this research addresses the following two inter-related objectives: (i) to identify the awareness, availability, and development of Big Data environments; and, (ii) to explore the opportunities and challenges associated with new Big Data-based decision support systems within retail organizations. The characteristics of an ideal data environment for Big Data adoption and development to take place are identified. The key findings reveal that while there was general awareness of the importance of Big Data, it was evident that the adoption and development of Big Data decision support was heavily reliant on a data environment that promotes transparency and a clear corporate data strategy.

Self-determined motivation for data-based decision-making: A relevance intervention in teacher training

While teachers’ core responsibility is to provide high-quality instruction, they are also expected to engage in data-based decision-making (DBDM), e.g., to analyse and use data to improve instructi...

Credit scoring using system log data in the internet bank

This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was improved by 2.4% points based on the K-S statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.

Embracing School Counselors’ Situatedness: Data-Based Decision Making as Fulfillment of a Complex Identity

Since at least the 1930s, school counselors have used data to inform school counseling programming. However, the evolving complexity of school counselors’ identity calls for an updated understanding of the use of data. We offer an expanded definition of data-based decision making that reflects the purpose of using data in educational settings and an appreciation of the complexity of the school counselor identity. We discuss implications for applying the data-based decision-making process using a multifaceted school counselor identity lens to support students’ success.