School accountability and teacher stress: international evidence from the OECD TALIS study

Accountability—the monitoring and use of student performance data to make judgements about school and teacher effectiveness—is increasing within school systems across the globe. In theory, by increasing accountability, the aims and incentives of governments, parents, school leaders and teachers become more closely aligned, potentially improving student achievement as a result. Yet, in practice, concerns are mounting about the stress that accountability is putting schools and teachers under. This paper presents new evidence on this issue, drawing upon data from more than 100,000 teachers across over 40 countries. We find evidence of a modest, positive correlation between school system accountability and how stressed teachers and headteachers are about this aspect of their job. When looking within schools, there is little evidence that the management practices of headteachers differ when they report feeling stressed about accountability, or that they transmit these feelings onto their staff. However, we do find strong evidence of ‘emotional contagion’ of stress amongst colleagues within schools, with teachers more likely to feel stressed by accountability if their colleagues do as well.

Personalizing Learning Targets With Technology-Based Assessment

There is tension between standardization and individualization in education today. Instructional fidelity to a challenging grade-level curriculum is the expectation of current pedagogy. Federal U.S. initiatives mandate assessing the academic growth of all students with common assessments aligned with challenging content standards. The growing number of students who vary as learners in today's classrooms holds implication for instruction and assessment. Personalizing learning targets promotes an equitable measure of student growth. Appropriate instructional goals develop grade-level curricular skills that are selected based on student performance data and are personalized with individualized baselines and proficiency targets. Technology facilitates data-driven instruction through the efficient development and progress monitoring of personalized learning goals. In this chapter, a technology-based model for personalizing standards-based learning targets, developing SMART goals, and monitoring progress is presented.

Driving Success in e-Learning Portals

Technological advancements are triggering disruptive inventions in the teaching and learning process. The introduction of massive online courses offers students many opportunities to enroll in any course they choose, transcending geographical barriers. However, online learning puts responsibility for learning on the learners and lack faculty-student and peer-peer direct interactions. The instructor's role also must be redefined to provide support and collaboration in the online environment. Recent research reports poor student retention and completion rates in such courses and there is a lack of effective frameworks and gap in research related to identifying key factors and finding solutions to these problems. A multi-faculty e-learning framework is proposed based on a theoretical model highlighting important factors to address these problems. Experimental results of an ANOVA analysis done on student performance data collected in a multi-faculty setup provided empirical evidence for its effectiveness in improving the student learning outcomes.

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

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.

Tracking Intervention Dosage to Inform Instructional Decision Making

Intervention dosage is an often-overlooked aspect of implementation fidelity. Tracking intervention dosage is critical for ensuring students with disabilities were present for and received the intervention for as many minutes as intended. Used in tandem with student performance data, intervention dosage can provide a clearer picture of intervention success. This article presents strategies for how teachers can plan for, collect, and use intervention dosage data to inform instructional data-based decision making.

Establishing a Sustainable Process to Measure Learner Performance

The quality of student learning and academic rigor is central to higher education. Nonetheless, colleges often prioritize metrics such as enrollment and graduation rates or use assessment data to solely fulfill accreditation requirements. The Academic Quality Assurance (AQA) team at a university ventured to expand the academic quality data landscape to learn more about student achievement. The paper shares the team’s journey to collect and report on student performance data for continuous improvement of academic programs. Specifically, this paper includes the methods to promote a culture of assessment by incorporating new concepts into the AQA process: Data visualization and storytelling with data. This paper includes the methodology to collect and report on data, samples of the systems and visualizations used, and the challenges faced in the context of people, process, and tools.

INTEGRATING TECHNOLOGY IN BASIC ENGLISH CLASS

This study aims to determine the extent to which technology can be applied in the process of teaching and learning Basic English and affect student performance. Data obtained from classroom observations and interviews. The analysis shows how the application of technology provides an improvement in the teaching and learning process. The application of technology particularly computer games give students the opportunity to practice using English, as well as increasing students' interest in participating in classroom activities. In the process of applying this technology the development of the ability of students' vocabulary mastery can be seen. In short, this study shows that the application of technology raises a positive impact on students in the learning process in the classroom.

NAPLAN versus In-School Assessment: How Similar or Different are Students’ Results?

The purpose of this study was to compare students’ results in mathematics from a large-scale standardized assessment, the National Assessment Program: Literacy and Numeracy (NAPLAN), with a set of teacher-developed, school-based assessments. A case study of an all-boys secondary school in Sydney, New South Wales, Australia, was conducted over three years with a total 1,456 student-participants. We found strong positive correlations existed between the NAPLAN data and certain school-based assessment data, such as monthly tests, but such results were not consistent across all classes. We conclude that NAPLAN data when considered in isolation, might be of limited benefit to teachers and students for diagnostic purposes. We therefore offer practical suggestions as to how student performance data generated from a large-scale assessment like NAPLAN might be best utilized and interpreted for formative assessment purposes in the school to optimally benefit individual students’ learning.