Use Of Educational Data Research Articles

Analysis of the State of Learning in University Students with the Use of a Hadoop Framework

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.

Data in practice: A participatory approach to understanding pre-service teachers’ perspectives

Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers’ perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers’ actions align with emerging learning analytics technologies, including the practices of pre-service teachers who are developing their perspectives on data use in classroom in their initial teacher education programme. This may lead to an integration gap in which technology and data literacy align poorly with expectations of the role of data and enabling technologies. This paper describes two participatory workshops that provide examples of the value of human-centred approaches to understand teachers’ perspectives on, and expectations for, data as evidence. These workshops focus on the design of pre-service teachers enrolled in teacher education programmes (N = 21) at two Australian universities. The approach points to the significance of (a) pre-service teachers’ intentions to track their students’ dispositions to learning and their ability to learn effectively, (b) the materiality of learning analytics as an enabling technology and (c) the alignment of learning analytics with learning design, including the human-centred, ethical and inclusive use of educational data in the teaching practice.
 
 Implications for practice or policy:
 
 Pre-service teachers ought to be given opportunities to engage and understand more about learning design, learning analytics and the use of data in classrooms.
 Professional experience placements for pre-service teachers should include participatory data sessions or learning design workshops.
 Teacher education academics in universities must be provided with ongoing professional development to support their preparation work of pre-service teachers’ data literacy, learning analytics and the increasing presence of data.

Complexity and then some: Theories of action and theories of learning in data-informed decision making

This article presents a synthesis of the complexity of using data in education and highlights some of the promise and challenges of adequately understanding data use across the continuum of accountability to instructional influence in the classroom. Several common themes around the complexity of data use were identified: (1) The nature of the data used, (2) the importance and difficulty of educator data capacity building and sense-making, (3) the importance of characteristics of collaboration, (4) the influence of contextual factors of schools, districts and international settings, (5) the potential negative influence of data use in equity considerations, and (6) the difficulty of determining impact of data use and discerning explanations of why data use initiative work some of the times were examined across the articles. This synthesis concludes with a call for an approach to research in data use in education that concurrently engages multiple aspects of the educational system with a focus on multiple levels of theories of learning that address students, teachers, and organizations.

Use of Educational Data Mining for the Prediction of Students’ Academic Performance

Use of Educational Data Mining for the Prediction of Students’ Academic Performance

Fintech Lenders’ Responses to Senate Probe Heighten Fears of Educational Redlining

The SBPC found that companies' use of information on credit applicants' education history resulted in ‘educational redlining,’ penalizing borrowers who attended minority-serving institutions. In response to our findings, five United States Senators wrote to a range of companies involved in the collection and use of educational data to demand detailed explanations of this data’s utilization in credit underwriting. These companies' responses only heightened concerns raised in our initial research and revealed additional gaps in fair lending oversight.

Data Driven Decision Making in Teacher Education: Perceptions of Pre-Service Teachers and Faculty Who Teach Them

ABSTRACT Integrating data driven decision making into teacher preparation programs has been called for by several researchers in order to help lessen the barriers teachers face in using data in their teaching. This mixed methods study examined the perceptions of data use in education by pre-service teachers (N = 112) and perceived knowledge and skills required for successful use from both pre-service teachers (N = 6) and the faculty who educate them (N = 5). Results have implications for teacher preparation programs considering data literacy integration and for researchers aiming to understand the best ways to encourage data driven decision-making skills and knowledge in pre-service teachers.

Research Topics on Educational Data Mining in MOOCS

Educational Data Mining Techniques have been widely used in MOOC environments to conduct different educational analyzes. In this context, a systematic mapping was conducted in five databases in order to verify which aspects of studies are inherent to the use of Educational Data Mining in MOOCs. The search comprised the period from 2015 to 2019, and 253 searches were found, out of this total, 133 studies were selected. The results revealed that studies on performance analysis, behavior analysis, forum analysis and implementation of recommendation systems are the most frequent themes.

Education and International Relations: A Data Analysis Project

A data-centered study drawing information from the CIA World Factbook dataset on the topic of Education. Methods used include visualizations, correlation analysis and clustering. More than 30 factors are compared with educational indexes and almost all countries are clustered. The dichotomy between developing and developed countries appears repeating in findings creating the foundation for future classification applications through the use of educational data. Moreover, it is determined that hygiene and social factors are more correlated to education than economic ones.

Big Data in Education: Perception of Training Advisors on Its Use in the Educational System

Big Data has revolutionized decision making in many fields, including education. The incorporation of information and communication technologies into education enables us to gather information about the teaching and learning process. As Big Data can help us improve it, it is paramount to integrate it into initial and continuous learning stages. This study therefore aims at finding out the perception of the training advisors of teacher training centers (N = 117) in Andalusia on the application of Big Data in education. The tool is an adaptation of the VABIDAE (Assessment of Big Data Applied to Education) scale, and the study of the descriptive statistics was carried out by using the analysis of variance (ANOVA) and Mann–Whitney U tests in order to check the existence of significant differences and correlations between the items that make up the scale. The results reflect the positive perception of training advisors on the use of Big Data in education. Significant differences were found in the competence level variable, whereby this tool was better rated by those advisors who feel that they have an advanced competence level. In conclusion, Big Data is valued for its ability to personalize educational processes and the consequent improvement in academic results, which shows the need to increase the level of knowledge about this tool.

Feeding back research results – Changes in principal and teacher narratives about student participation

Schools are among the most studied organizations, yet research has little influence on them. This is in part because schools often do not have an opportunity to learn from the research projects they are engaged in. In this paper, we explain the process and impact of feeding back research results to participating schools. The feedback of results related to the topic of student participation was designed in a school-specific way and expected to support school improvement processes. Accordingly, we examined changes in beliefs and school practices by analysing longitudinal qualitative interviews. Results show changes in school practices and in the perceived importance of student participation, as well as enduring beliefs that inhibited changes. The findings of this paper contribute to the discourse on data use in education and to building research-practice-partnerships.

Is Data Dark? Lessons from Borges’s “Funes the Memorius”

In Funes the Memorius Jorge Luis Borges tells the tale of an Argentinian man who falls off a horse, becomes paralyzed, but with his misfortune acquires the strange gift of infinite memory. Funes remembers everything, which is to say he forgets nothing. l will use Borges' story as the backdrop for my response to Professor Selwyn.
 My commentary is in three parts. First, I begin by stating some core areas of agreement, of which there are many. Second, I examine Selwyn’s use of the word “data”. I argue that it perpetuates a number of common misconceptions about statistics and the scientific method. We cannot understand the importance of learning analytics without first clarifying these misconceptions and moving beyond them. In the course of my argument I challenge Selwyn’s central thesis that “Education is inherently social, inherently contextual, inherently subjective; it means you can’t objectively rate it, measure it, indicate it.” Third, I turn the table on Selwyn. As a critic of learning analytics Selwyn suggests that data “disadvantages large numbers of people”. I argue that the root problem in education is the status quo, which Selwyn unwittingly represents, and not learning analytics. If we care about equity in education, as part of a broader interest in social justice, then learning analytics and the use of educational data can be a powerful instrument for empowering the disadvantaged.

The Use of Educational Data Mining Technique in Technology Assimilation Evaluation: A Response Assessment

The Use of Educational Data Mining Technique in Technology Assimilation Evaluation: A Response Assessment

Data-based decision-making for school improvement: Research insights and gaps

ABSTRACTBackground: Data-based decision-making in education often focuses on the use of summative assessment data in order to bring about improvements in student achievement. However, many other sources of evidence are available across a wide range of indicators. There is potential for school leaders, teachers and students to use these diverse sources more fully to support their work on a range of school improvement goals.Purpose and sources of evidence: To explore data-based decision-making for school improvement, this theoretical paper discusses recent research and literature from different areas of data use in education. These areas include the use of formative assessment data, educational research study findings and ‘big data’. In particular, the discussion focuses on how school leaders and teachers can use different sources of data to improve the quality of education.Main argument: Based on the literature reviewed, an iterative model of data use for school improvement is described, consisting of defining goals for data use, collecting different types of data or evidence (e.g. formal data, informal data, research evidence and ‘big data’), sense-making, taking improvement actions and evaluation. Drawing on the literature, research insights are discussed for each of these components, as well as identification of the research gaps that still exist. It is noted that the process of data use does not happen in isolation: data use is influenced by system, organisation and team/individual level factors.Conclusions: When it comes to using data to improve the quality of teaching and learning, it is evident that some of the most important enablers and barriers include data literacy and leadership. However, what is less well understood is how we can promote the enablers and remove the barriers to unlock, more fully, the potential of data use. Only then can data use lead to sustainable school improvement.

Expanding the Use of Educational Data for Social Justice: Lessons From the Texas Cap on Special Education and Implications for Practitioner–Scholar Preparation

Educational data offer a powerful tool for supporting equity. In this article, the authors call for a shift toward greater use of data in educator preparation programs. The authors motivate their proposal by highlighting findings from the Department of Education report released January 2018, which found that the Texas Education Agency systematically denied students special education services. The article outlines three basic metrics that stakeholders can use to identify potential noncompliance with the Individuals With Disabilities Education Act. The authors describe other ways data can be used to promote equity and close with recommendations for educator preparation policy and practice.

The Australasian Virtual Herbarium (AVH) and the Changing Role of Herbaria

Australia’s Virtual Herbarium (AVH) was created in 2001 and developed between 2001 and 2006 with the databasing of the label data of specimens from the Australian Commonwealth, state and territory herbaria. The success of AVH ultimately led to funding for the Atlas of Living Australia (ALA) and in October 2012 AVH was re-launched as part of the ALA infrastructure (http://avh.chah.org.au). Since 2012, some university herbaria have also joined AVH and in June 2017 the New Zealand Virtual Herbarium (NZVH) was integrated, giving rise to the Australasian Virtual Herbarium. The AVH currently holds almost 6.3 million records from 23 Australian and New Zealand herbaria and comprises over 80 per cent of the vouchered occurrence records of Australian plants, algae and fungi in ALA. While no longer at the forefront of the digitisation wave, having been around for so long, AVH can provide valuable insights into how and to what extent herbarium data is used and into the benefits of digitisation for herbaria and natural history collections. Six years of download statistics shows an ever-increasing use of AVH data and a widening of the user base. A far greater proportion of downloaded records is ostensibly used for new uses like ecological research and conservation, than for the traditional uses of herbarium specimen data, systematics and collections management. The last few years have also seen an increase in the use of AVH data in education. Over the years many research articles based on AVH data have been written, mostly at the interface between ecology and systematics, i.e. biogeography and conservation science. With the increase of the types of uses for herbarium data, it becomes important, also for the herbaria themselves, to consider the fitness for purpose of AVH data for uses other than the purpose for which it was collected. It has become apparent that the uncertainty of the latitudes and longitude provided with herbarium specimens is often too great for use in ecological research or conservation and there is a bias in collection density at the bioregion level, which is likely to be more severe at smaller spatial scales. Also, while the demand for vouchered occurrence records is increasing, we have seen a steady decrease in the intake of new specimens by Australian herbaria since the 1980s. This presentation will cover how Australian herbaria use AVH, how other users use AVH and what being part of AVH has meant to Australian herbaria.

On meeting students where they are: Teacher judgment and the use of data in higher education

It is treated as a truism that teaching well requires ‘meeting students where they are’. Data enable us to know better where that is. Data can improve instructional practice by informing predictions about which pedagogies will be most successful for which students, and it can improve advising practice by informing predictions about which students are likely to thrive on which pathways moving forward. But moral hazards lurk, and these have been highlighted especially in response to the burgeoning use of new data mining technologies to produce ‘big data’. This article explores the ethics of data use in higher education. I consider the ethics of aggregate data as a tool for meeting students where they are, comparing it to an ongoing debate about the use of statistics in the legal context. The comparison generates two important insights: First, even the most viable moral concerns about using statistical information in the educational context are not deal-breakers: Those concerns should lead us to exercise careful judgment in the use of statistical information but do not justify eschewing that information altogether. Second, surprisingly, those viable moral concerns show big data to have a moral advantage over traditional little data, suggesting that some of the resistance to the use of big data in education is either unfounded or at least needs to be balanced against the moral advantages big data offer.

School-based multi-tiered systems of support (MTSS): An introduction to MTSS for neuropsychologists

ABSTRACTWhen completing pediatric neuropsychological evaluations, it is important for clinicians to incorporate data from children’s school environments. Today, the educational data available to neuropsychologists have resulted from the utilization of multi-tiered systems of support (MTSS) within schools; however, practitioners may lack knowledge and/or training in this school-based approach to assessment and intervention. Better understanding of MTSS may aid pediatric neuropsychologists in their interpretation and use of educational data commonly provided by schools. Thus, the purpose of this article is to provide an overview of school-based MTSS and explain how knowledge about MTSS can inform neuropsychological evaluations. Specifically, practical implications for record reviews, recommendation development, and consultation with schools will be discussed.

19 Times Data Analysis Empowered Students and Schools: Which Students Succeed and Why?

Thoughtful use of education data has tremendous potential to improve and address inequities in America’s education system. Scientists better understand how the brain incorporates new information and skills. Educators have a more accurate sense of student progress and potential risk for dropping out. Students and teachers use more detailed information about their strengths, weaknesses, and individual academic performance to diagnose and address learning gaps. Schools can correlate patterns with failing or dropping out, and intervene early with at-risk students. Districts and schools can use data to allocate resources and create institutional reform to better meet student needs in a world where students take increasingly personalized or non-traditional paths to graduation. Today, companies use new technologies and analytical approaches to provide more personalized services to customers; municipalities streamline allocation of city resources; and doctors can make more precise diagnoses. Until recently, educators did not have much information available to generate new insights about the learning process, student success, or optimal institutional management. However, education — both K-12 and higher ed — is now catching up as newly created digital learning tools and platforms generate an abundance of highly detailed data that can help parents, teachers, administrators — and ultimately students — make more informed choices. With this data, we can finally observe patterns during instruction, across classrooms, between schools, and over time to create a more complete understanding of which students succeed and why. The ability to compare administrative, academic, demographic, and social information from various sources at last provides a means to examine the full multiplicity of factors that contribute to student success. Examining student trajectories over time shows how well students are prepared for the next steps in the learning process. They can identify ways to facilitate application and transition between school levels and into the workforce. To provide useful insights, research about long term education and career success often requires sharing information — sometimes including sensitive data — across schools, between states, and over time. The student data used in this research must be collected, used, and deleted with sufficient privacy protections. Appropriate policies, well-tailored laws, best practices, and genuine enforcement mechanism minimize the privacy risks, and ensure the best outcomes for students. This report highlights the ways that newly available technology, data, and analytical techniques can create better educational outcomes. It presents concrete examples from Pre-K through higher education of how education data can be used to benefit students, the education system, and society-at-large. The cases here illustrate how students, educators, researchers, and advocates apply data analysis to encourage student success and retention, facilitate more efective instruction, advising, and administration, and ameliorate inequalities. Data-driven education has the potential help bridge the achievement, retention, and discipline gaps so that all students can enjoy a high quality education and achieve career success.

Investigative modes in research on data use in education

ABSTRACTThis article reports on the findings from a literature review of research on data use in education published in English, German and Scandinavian languages. The review is inspired by methods for systematic mapping. The analysis illustrates how the characteristics of the total corpus of 129 articles on data use in education vary across different contexts, countries and regions. In all contexts, the studies primarily investigate structures and systems around data use. While the Anglophone studies are mainly empirical and often concerned with implementation and effectiveness in terms of data use, the studies published in German and Scandinavian languages focus more heavily on discussions and analytical reflections upon the developments of data use in education. Six investigative modes of studies on data use that can contribute to creating a more nuanced understanding of research on data use in education are identified, presented and discussed.

The effects of a data use intervention on educators\u2019 satisfaction and data literacy

Schools in many different countries are increasingly expected to use data for school improvement. However, schools struggle with the implementation of data use, because building human capacity around data use in education has not received enough attention. Educators urgently need to develop data literacy skills for being able to use data. For supporting schools with the endeavor of developing data literacy skills, we developed and implemented a data use intervention in secondary schools based in the Netherlands. This study therefore focuses on the effects of this intervention on educator satisfaction with the intervention and their data literacy skills and attitude toward data use. This study uses a quasi-experimental research design and employs a mixed-methods approach with a data use questionnaire filled in by data team schools (N = 9) and comparison schools (N = 42), a satisfaction questionnaire filled in by data team participants (N = 55), pre- and posttest knowledge tests filled in by data team participants (N = 36), and interview data (N = 11) from three case study schools. The results show that the participants were, for example, very satisfied with the support received during the intervention. Also, respondents developed new data literacy skills and showed a more positive attitude toward data use. The results show how teachers can be supported systematically in data use in their educational practice. In the conclusions, we discuss some important implications for practice regarding the intensity and duration of support and implications for further research.