scholarly journals Linking Instruction and Student Achievement. A research design for a new generation of classroom studies

2017 ◽  
Vol 11 (3) ◽  
pp. 10 ◽  
Author(s):  
Kirsti Klette ◽  
Marte Blikstad-Balas ◽  
Astrid Roe
Keyword(s):  
Student Achievement ◽  
Large Scale ◽  
Classroom Practices ◽  
Instructional Quality ◽  
Theoretical Background ◽  
Video Data ◽  
Data Sources ◽  
Future Research ◽  
Ongoing Research ◽  
Achievement Data

AbstractEducational research into instructional quality would benefit from macro- and meso-level instructional data – such as achievement data or large-scale student surveys – in relation to data from the micro level – such as detailed analyses of classroom practices. Several scholars have specifically asked for studies that correlate achievement data with records of learning processes and teaching strategies, and ongoing projects attempting to do so have shown promising results. Linking different data sources on instructional quality is quite demanding because it requires a concerted effort by researchers from different fields of expertise and different traditions. A main ambition of our ongoing research project is precisely to advance such integration. As the title of the project reveals, we are dedicated to Linking Instruction and Student Achievement (LISA). In this article, we start by providing a theoretical background and status of knowledge related to instructional quality. We go on to argue that video data has shown particular promise in studies aiming to obtain systematic data from a range of classrooms in order to compare classroom practices. We then present the three components of the LISA project’s design – student perception surveys, systematic classroom observation, and achievement gains in national tests – and the value of combining these three data sources. Finally, we will outline some of our findings thus far and point to future research possibilities.Key words: instructional quality; classroom practices; video studies; mathematics; language arts Å koble undervisning med elevprestasjoner - Forskningsdesign for en ny generasjon klasseromsstudierSammendragFor å studere undervisningskvalitet vil det være en fordel å kombinere data fra et makro og meso- nivå  med detaljerte studier av hva som skjer i klasserommet. Flere har etterlyst studier som ser på sammenhenger mellom målbar faglig fremgang og lærerens undervisning. Å få til slike studier er krevende, da det forutsetter et tett samarbeid mellom forskere fra ulike felt med ulik ekspertise innenfor nokså ulike forskningstradisjoner. En hovedambisjon i vårt pågående forskningsprosjekt er nettopp å få til en slik integrasjon. Som tittelen avslører, er vi dedikert til «Linking Instruction and Student Achievement (LISA)». I denne artikkelen presenterer vi det teoretiske og empiriske grunnlaget knyttet til undervisningskvalitet. Videre argumenterer vi for verdien av videodata i studier som sammenligner undervisningspraksiser fra ulike klasserom på en systematisk måte. Deretter presenterer vi de tre datakildene i LISA-prosjektets forskningsdesign – spørreskjemaer til elever om deres oppfatninger om lærerens undervisning, systematiske klasseromsobservasjoner, og målt fremgang på nasjonale prøver i lesing og regning. Verdien av å kombinere nettopp disse tre datakildene vil også bli diskutert. Avslutningsvis deler vi noen av våre tidlige forskningsfunn.Nøkkelord: undervisningskvalitet; klasseromspraksis; video studier; matematikk; norskfaget

Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder

2020 ◽  
pp. 154-165
Author(s):  
Pattabiraman V. ◽  
Parvathi R.
Keyword(s):  
Large Scale ◽  
Curse Of Dimensionality ◽  
Data Sources ◽  
Sensor Data ◽  
Future Research ◽  
Computational Power ◽  
Nonlinear Methods ◽  
Research Areas ◽  
Learning Techniques ◽  
Natural Data

Natural data erupting directly out of various data sources, such as text, image, video, audio, and sensor data, comes with an inherent property of having very large dimensions or features of the data. While these features add richness and perspectives to the data, due to sparsity associated with them, it adds to the computational complexity while learning, unable to visualize and interpret them, thus requiring large scale computational power to make insights out of it. This is famously called “curse of dimensionality.” This chapter discusses the methods by which curse of dimensionality is cured using conventional methods and analyzes its performance for given complex datasets. It also discusses the advantages of nonlinear methods over linear methods and neural networks, which could be a better approach when compared to other nonlinear methods. It also discusses future research areas such as application of deep learning techniques, which can be applied as a cure for this curse.

scholarly journals GrandBase: generating actionable knowledge from Big Data

2017 ◽  
Vol 1 (2) ◽  
pp. 105-126 ◽  
Author(s):  
Xiu Susie Fang ◽  
Quan Z. Sheng ◽  
Xianzhi Wang ◽  
Anne H.H. Ngu ◽  
Yihong Zhang
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Empirical Studies ◽  
Knowledge Extraction ◽  
Data Sources ◽  
Future Research ◽  
Content Type ◽  
Actionable Knowledge ◽  
True Value ◽  
Truth Discovery

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

scholarly journals Understanding the phenomenology of reading through modelling

Semantic Web ◽  
2020 ◽  
pp. 1-27
Author(s):  
Alessio Antonini ◽  
Mari Carmen Suárez-Figueroa ◽  
Alessandro Adamou ◽  
Francesca Benatti ◽  
François Vignale ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Conceptual Model ◽  
Large Scale ◽  
Research Field ◽  
Future Research ◽  
Ongoing Research ◽  
Reading Experience ◽  
Macro Scale ◽  
Support Of Research ◽  
Data Investigation

Large scale cultural heritage datasets and computational methods for the Humanities research framework are the two pillars of Digital Humanities (DH), a research field aiming to expand Humanities studies beyond specific sources and periods to address macro-scale research questions on broad human phenomena. In this regard, the development of machine-readable semantically enriched data models based on a cross-disciplinary “language” of phenomena is critical for achieving the interoperability of research data. This paper reports on, documents, and discusses the development of a model for the study of reading experiences as part of the EU JPI-CH project Reading Europe Advanced Data Investigation Tool (READ-IT). Through the discussion of the READ-IT ontology of reading experience, this contribution will highlight and address three challenges emerging from the development of a conceptual model for the support of research on cultural heritage. Firstly, this contribution addresses modelling for multi-disciplinary research. Secondly, this work describes the development of an ontology of reading experience, under the light of the experience of previous projects, and of ongoing and future research developments. Lastly, this contribution addresses the validation of a conceptual model in the context of ongoing research, the lack of a consolidated set of theories and of a consensus of domain experts.

Distorting Value Added: The Use of Longitudinal, Vertically Scaled Student Achievement Data for Growth-Based, Value-Added Accountability

2006 ◽  
Vol 31 (1) ◽  
pp. 35-62 ◽  
Author(s):  
Joseph A. Martineau
Keyword(s):  
Student Achievement ◽  
Student Performance ◽  
Value Added ◽  
Student Growth ◽  
Future Research ◽  
Achievement Data ◽  
Value Added Models ◽  
Value Added Modeling ◽  
Construct Shift ◽  
Vertical Scales

Longitudinal, student performance-based, value-added accountability models have become popular of late and continue to enjoy increasing popularity. Such models require student data to be vertically scaled across wide grade and developmental ranges so that the value added to student growth/achievement by teachers, schools, and districts may be modeled in an accurate manner. Many assessment companies provide such vertical scales and claim that those scales are adequate for longitudinal value-added modeling. However, psychometricians tend to agree that scales spanning wide grade/developmental ranges also span wide content ranges, and that scores cannot be considered exchangeable along the various portions of the scale. This shift in the constructs being measured from grade to grade jeopardizes the validity of inferences made from longitudinal value-added models. This study demonstrates mathematically that the use of such “construct-shifting” vertical scales in longitudinal, value-added models introduces remarkable distortions in the value-added estimates of the majority of educators. These distortions include (a) identification of effective teachers/schools as ineffective (and vice versa) simply because their students’ achievement is outside the developmental range measured well by “appropriate” grade-level tests, and (b) the attribution of prior teacher/school effects to later teachers/schools. Therefore, theories, models, policies, rewards, and sanctions based upon such value-added estimates are likely to be invalid because of distorted conclusions about educator effectiveness in eliciting student growth. This study identifies highly restrictive scenarios in which current value-added models can be validly applied in high-stakes and low-stakes research uses. This article further identifies one use of student achievement data for growth-based, value-added modeling that is not plagued by the problems of construct shift: the assessment of an upper grade content (e.g., fourth grade) in both the grade below and the appropriate grade to obtain a measure of student gain on a grade-specific mix of constructs. Directions for future research on methods to alleviate the problems of construct shift are identified as well.

scholarly journals School District Contributions to Students' Math and Language Achievement

2019 ◽  
Vol 14 (9) ◽  
Author(s):  
Victoria Handford ◽  
Kenneth Leithwood
Keyword(s):  
Student Achievement ◽  
Mixed Methods ◽  
Large Scale ◽  
School Administrators ◽  
Mixed Methods Study ◽  
School Level ◽  
Language Test ◽  
Semistructured Interview ◽  
District Leaders ◽  
Achievement Data

Conducted in British Columbia, this mixed-methods study tested the effects of nine district characteristics on student achievement, explored conditions that mediate the effects of such characteristics, and contributed to understandings about the role school-level leaders play in district efforts to improve achievement. Semistructured interview data from 37 school administrators provided qualitative data. Quantitative data were provided by the responses of 998 school and district leaders’ in 21 districts to two surveys. Student achievement data were district-level results of elementary and secondary student provincial math and language test scores. All nine district characteristics contributed significantly to student achievement. Three conditions served as especially powerful mediators of such district effects. The same conditions, as well as others, acted as significant mediators of school-level leader effects on achievement. This is among the few large-scale mixed-methods studies identifying characteristics of districts explaining variation in student achievement.

scholarly journals Identifying Effective Classroom Practices Using Student Achievement Data

2010 ◽  
Author(s):  
Thomas Kane ◽  
Eric Taylor ◽  
John Tyler ◽  
Amy Wooten
Keyword(s):  
Student Achievement ◽  
Classroom Practices ◽  
Achievement Data

Identifying Effective Classroom Practices Using Student Achievement Data

2011 ◽  
Vol 46 (3) ◽  
pp. 587-613 ◽  
Author(s):  
Thomas J. Kane ◽  
Eric S. Taylor ◽  
John H. Tyler ◽  
Amy L. Wooten
Keyword(s):  
Student Achievement ◽  
Classroom Practices ◽  
Achievement Data

scholarly journals Semantic Networks for Engineering Design: State of the Art and Future Directions

2021 ◽  
pp. 1-45
Author(s):  
Ji Han ◽  
Serhad Sarica ◽  
Feng Shi ◽  
Jianxi Luo
Keyword(s):  
Engineering Design ◽  
Large Scale ◽  
Design Research ◽  
State Of The Art ◽  
Semantic Network ◽  
Semantic Networks ◽  
Knowledge Bases ◽  
Data Sources ◽  
Future Research ◽  
Support Engineering

Abstract In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Decision Making for the Collaborative Energy Supply System of Oregon and Washington

2015 ◽  
Author(s):  
Bryony DuPont ◽  
Ridwan Azam ◽  
Scott Proper ◽  
Eduardo Cotilla-Sanchez ◽  
Christopher Hoyle ◽  
...  
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Power Supply ◽  
Large Scale ◽  
Energy System ◽  
The United States ◽  
Supply System ◽  
Future Research ◽  
Ongoing Research ◽  
Systems Research

As demand for electricity in the United States continues to increase, it is necessary to explore the means through which the modern power supply system can accommodate both increasing affluence (which is accompanied by increased per-capita consumption) and the continually growing global population. Though there has been a great deal of research into the theoretical optimization of large-scale power systems, research into the use of an existing power system as a foundation for this growth has yet to be fully explored. Current successful and robust power generation systems that have significant renewable energy penetration — despite not having been optimized a priori — can be used to inform the advancement of modern power systems to accommodate the increasing demand for electricity. Leveraging ongoing research projects at Oregon State University and the National Energy Technology Laboratory, this work explores how an accurate and state-of-the-art computational model of the Oregon/Washington (OR/WA) energy system can be employed as part of an overarching power systems optimization scheme that looks to inform the decision making process for next generation power supply systems. Research scenarios that explore an introductory multi-objective power flow analysis for the OR/WA grid will be shown, along with a discussion of future research directions.

Teachers’ Epistemological World Views and Educational Practices

2003 ◽  
Vol 3 (2) ◽  
pp. 178-235 ◽  
Author(s):  
Gregory Schraw ◽  
Lori Olafson
Keyword(s):  
Teacher Training ◽  
Environmental Factors ◽  
Instructional Practices ◽  
Classroom Practices ◽  
World View ◽  
Future Research ◽  
Instructional Decisions ◽  
World Views ◽  
Ongoing Research ◽  
Teachers Beliefs

This article examines the implications of teachers’ beliefs about knowledge. We compare three epistemological world views we refer to as realist, contextualist, and relativist. An epistemological world view is a set of beliefs about knowledge and knowledge acquisition that influences the way teachers think and make important instructional decisions. We assume that different epistemological world views lead to different choices about curriculum, pedagogy, and assessment. We describe ongoing research that examines the beliefs held by teachers, instructional practices, and the consistency between beliefs and classroom practices. We summarize findings from our research and discuss their implications for teacher training. We also consider environmental factors such as school culture and mandated standards that affect teachers’ beliefs. We relate our findings to implications for teacher training. We also identify directions for future research.

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