Comparing Online and Traditional Assessment Practices in Middle School Mathematics

As a result of the COVID-19 pandemic, teachers across the world have been forced to explore different modalities of assessment, many of them virtual. Now that many of the most restricting policies for schools due to pandemic have been lifted, the use of these virtual assessments remain. Due to the recent nature of their use though means that not many studies have looked into the implications of these assessments on students let alone middle school students. This study aims to help fill in some of the gaps in this research. In this study, students will take one of two assessments with the exact same questions. One of the assessments will be paper and pencil, the other will be on Google Form. Students will be randomized into which modality they will complete and then the scores of the two groups will be compared. The goal of this is to determine if taking an assessment virtually provides a roadblock to displaying mastery of learning for the students. After students are done with their assessment, they will then take a short survey asking them how hard they perceived their assessment to be. The results of this study will hopefully tell us two things: whether one assessment is more challenging based on the scores and based off student’s perceptions

The Effects of a Computer Based Program on Student Mathematics Achievement Within an Urban Middle School in Georgia

The purpose of this quantitative study was to investigate the effects of adaptive computer-assisted instruction (CAI) on student mathematics achievement. The researcher sought to describe factors that may influence academic achievement for eight-grade students. The instruments used to gather data were post curriculum-based mathematics benchmark assessment data administered during fall, winter, and spring semesters, the spring mathematic assessment for the Georgia Milestones, and open and close-ended questionnaires. A purposeful sampling of 63 students were chosen to complete questionnaires. Data analyzed from the 2018 Mathematics scores from SchoolCity and the Georgia Milestones revealed that the SuccessMaker® online adaptive software tool positively impacted student mathematics achievement. The questionnaire responses showed that 100% of the teachers believed the online tutoring software to effective in improving student mathematics skills. Only 50% of the student participants rated the program as effective. Additionally, the students who received teacher and parental support with using SuccessMaker® obtained higher scores on the standardized assessment, Georgia Milestones

An Evaluation of Computer Aided Learning (BRAC-CAL) in Secondary Schools in Bangladesh.

BRAC initiated Computer Aided Learning (CAL) programme, the first ever in Bangladesh, to introduce ICT based materials in teaching-learning in 2004 Along with digital contents of Science, English and mathematics of secondary level, this programme provided basic ICT and content delivery training to the teachers of programme schools. A qualitative evaluation following the Realist Evaluation framework was designed to evaluate the programme mechanism, context and outcome. Data were collected from six secondary schools selected purposively. Findings showed that both teachers and students enjoyed the CAL materials and also believed that those materials had changed classroom scenario by improving learners’ attention and participation in classroom activities. However, significant difference was not observed between CAL and non-CAL classrooms. Teachers struggled to organise collaborative learning tasks such as group and pair works. Students also had limited participation in teaching-learning process. Irregular electricity supply sometimes hampered use of CAL materials. Furthermore, students had limited access to these materials. Bearing this context the recommendations were to focus more on teachers’ pedagogic improvement and to create more scopes for students’ self use of these materials

Building a Model of Collaboration Between Historically Black and Historically White Universities

Despite increases over the last two decades in the number of degrees awarded to students from underrepresented groups in science, technology, engineering, and mathematics (STEM) disciplines, enhancing diversity in these disciplines remains a challenge. This article describes a strategic approach to this challenge—the development of a collaborative partnership between two universities: the historically Black Elizabeth City State University and the historically White University of New Hampshire. The partnership, a type of learning organization built on three mutually agreed upon principles, strives to enhance opportunities for underrepresented students to pursue careers in the STEM disciplines. This article further describes six promising practices that framed the partnership, which resulted in the submission of nine proposals to federal agencies and the funding of four grants that led to the implementation, research, learning, and evaluation that followed

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

A systematic review of pedagogical approaches that can effectively include children with special educational needs in mainstream classrooms with a particular focus on peer group interactive approaches

The broad background to this review is a long history of concepts of special pupils and special education, and a faith in special pedagogical approaches. The rise of inclusive schools and some important critiques of special pedagogy (e.g. Hart, 1996; Norwich and Lewis, 2001; Thomas and Loxley, 2001) have raised the profile of teaching approaches that ordinary teachers can and do use to include children with special educational needs in mainstream classrooms. Inclusive education itself is increasingly conceived as being about the quality of learning and participation that goes on in inclusive schools rather than simplistic matters of where children are place

What\u27s in a Label? Unpacking the Meaning of Achievement Labels from Tests

As a result of federal accountability policies, achievement level labels from statewide assessments are ascribed to public school students 17 times between grades 3 and 12. Depending on students’ performance and state of residence, they may be labeled inadequate or in need of support, below proficient or approaching expectations, level 3 or on track—to name a few examples. These labels are delivered through individual reports for students and parents as well as group reports for teachers. In spite of their widespread use, research on how achievement level labels are interpreted is minimal. The aim of this study was to improve the current understanding of how teachers, parents, and students make sense of such labels to promote better-informed labeling decisions. To that end, teachers (N = 51) and parents (N = 50) completed an online survey that involved sorting tasks, scale ratings, top-three selections, and open-ended questions. Meanwhile, students (N = 24) participated in semi-structured interviews that included a brief survey component. Achievement level labels for statewide assessments from all 50 states were investigated. Since some states use the same labels, there were 28 unique labels for the lowest level of achievement (“Lowest”), 18 for the level denoting proficiency (“Medium”), and 27 for the one or two levels between those categories (“Low”). Multidimensional scaling revealed key dimensions that distinguished the labels within each set from one another, including the use of specific words as well as differences in tone. The findings also suggest that some Low and Medium labels denoting the same level of achievement in fact imply different achievement levels. For instance, approaching proficient was perceived as indicating substantially more achievement than basic; the same was true for standard met compared to sufficient command. Additionally, some labels were perceived as more encouraging (e.g., in need of support, not yet meeting expectations) and others as clearer than their counterparts (e.g., standard not met, pass). Teachers’, parents’, and students’ perceptions and preferences were similar, with a few exceptions that are discussed in detail along with students’ comments about their preferred labels and labeling advice from teachers and parents. Recommendations are provided