If You Build It Will They Come? Teacher Use of Student Performance Data on a Web-Based Tool

1986 ◽

Vol 8 (3) ◽

pp. 5-13 ◽

Cited By ~ 5

Author(s):

Lynn S. Fuchs ◽

Douglas Fuchs

Keyword(s):

Student Performance ◽

Analysis Of Variance ◽

Meta Analysis ◽

Performance Data ◽

Effect Sizes ◽

Education Practice ◽

Equal Interval ◽

Data Source ◽

Student Performance Data ◽

Over Time

This meta-analysis investigated the effects on achievement of type of graphing paper employed in displaying student performance data collected over time. The data source was 15 controlled studies with 16 effect sizes. The average weighted unbiased effect sizes for 6-cycle and equal interval paper, respectively, were .53 and .46. Hedges's analogue to analysis of variance indicated this difference was not statistically reliable. Implications for special education practice are discussed.

Download Full-text

If You Build it Will They Come? Teachers’ Online Use of Student Performance Data

Education Finance and Policy ◽

10.1162/edfp_a_00089 ◽

2013 ◽

Vol 8 (2) ◽

pp. 168-207 ◽

Cited By ~ 6

Author(s):

John H. Tyler

Keyword(s):

Student Performance ◽

Test Data ◽

Effective Teacher ◽

Urban District ◽

Web Based ◽

Achievement Gains ◽

Computer Based ◽

Student Test ◽

Teacher Data ◽

Student Performance Data

Testing of students and computer systems to store, manage, analyze, and report the resulting test data have grown hand-in-hand. Extant research on teacher use of electronically stored data are largely qualitative and focused on the conditions necessary (but not sufficient) for effective teacher data use. Absent from the research is objective information on how much and in what ways teachers use computer-based student test data, even when supposed precursors of usage are in place. This paper addresses this knowledge gap by analyzing the online activities of teachers in one mid-size urban district. Utilizing Web logs collected between 2008 and 2010, I find low teacher interaction with Web-based pages that contain student test information that could potentially inform practice. I also find no evidence that teacher usage of Web-based student data are related to student achievement gains, but there is reason to believe these estimates are downwardly biased.

Download Full-text

Implementasi Fuzzy C-Means dan Possibilistik C-Means Pada Data Performance Mahasiswa

Unisda Journal of Mathematics and Computer Science (UJMC) ◽

10.52166/ujmc.v6i2.2392 ◽

2020 ◽

Vol 6 (2) ◽

pp. 39-48

Author(s):

Gadis Retno Apsari ◽

Mohammad Syaiful Pradana ◽

Novita Eka Chandra

Keyword(s):

Objective Function ◽

Grade Point Average ◽

Student Performance ◽

Fuzzy Clustering ◽

Performance Data ◽

Grade Point ◽

Fuzzy C Means ◽

Student Performance Data ◽

Minimum Objective Function

Students are the most important component in a university, especially private universities especially Universitas Islam Darul ‘ulum (Unisda) Lamongan. One of the most important roles of students for higher education is achievement. This study aims to determine the role of Fuzzy Clustering in classifying student performance data. The data includes GPA (Grade Point Average), ECCU (Extra-Curricular Credit Unit), attendance, and students' willingness to learn. So that groups of students who have the potential to have achievements can be identified. In this case, the grouping of student performance data uses Fuzzy Clustering by applying the Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) algorithms with the help of Matlab. In the FCM algorithm, the membership degree is updated so as to produce a minimum objective function value. Meanwhile, the PCM algorithm uses a T matrix, which shows the peculiarities of the data which are also based on minimizing the objective function.

Download Full-text

Mapping a Course for Improved Student Learning: How Innovative Schools Systematically Use Student Performance Data to Guide Improvement

PsycEXTRA Dataset ◽

10.1037/e382752004-001 ◽

2003 ◽

Cited By ~ 18

Author(s):

Jonathan A. Supovitz ◽

Valerie Klein

Keyword(s):

Student Learning ◽

Student Performance ◽

Performance Data ◽

Innovative Schools ◽

Student Performance Data ◽

Improved Student Learning

Download Full-text

PerformanceVis: Visual analytics of student performance data from an introductory chemistry course

Visual Informatics ◽

10.1016/j.visinf.2019.10.004 ◽

2019 ◽

Vol 3 (4) ◽

pp. 166-176

Author(s):

Haozhang Deng ◽

Xuemeng Wang ◽

Zhiyi Guo ◽

Ashley Decker ◽

Xiaojing Duan ◽

...

Keyword(s):

Student Performance ◽

Visual Analytics ◽

Performance Data ◽

Introductory Chemistry ◽

Student Performance Data

Download Full-text

PLCs on steroids

Phi Delta Kappan ◽

10.1177/0031721717690369 ◽

2017 ◽

Vol 98 (5) ◽

pp. 67-71

Author(s):

Michael J. Wasta

Keyword(s):

Professional Learning Communities ◽

Learning Communities ◽

Student Performance ◽

Professional Learning ◽

Teacher Practice ◽

Performance Data ◽

Team Members ◽

Improve Instruction ◽

Student Performance Data

Research on educators’ professional learning communities (PLCs) suggest that while they often help teachers to make sense of student performance data, they tend to spend relatively little time studying what teachers actually do in the classroom. Evidence suggests that, given modest amounts of guidance and support, PLCs can collect useful data on teacher practice, and team members can identify specific, actionable ways in which to improve instruction.

Download Full-text