Intensive Interventions in Reading for Students with Reading Disabilities: Meaningful Impacts

We use three data sources to build a rationale for why intensive interventions are necessary for students with pervasive reading disabilities: current data on the performance of students with disabilities on reading achievement measures over time, observation studies on students with reading disabilities in general and special education classrooms, and findings from intensive intervention studies for students with reading disabilities. Results of these data sources indicate that students with disabilities are not making progress in reading at the same rate as students without disabilities, reading instruction for students with reading disabilities is comprised of excessive amounts of low level tasks, and findings from intensive intervention studies suggest positive impacts for students with reading disabilities. We argue that students with reading disabilities require ongoing intensive interventions that are likely to require schools to change the contexts and practices for these students.     

Understanding Children's Comprehension of Visual Displays of Complex Information

Developing students' ability to interpret the vast amount of quantitative data they encounter on a daily basis has become a major task for today's educators. However, very little attention has been given to students' strategies of analyzing multivariate data. This study investigated how students interpret and analyze multivariate data organized in tables and the nature of external visual displays that they tend to create and use for this purpose. Ten middle school students were asked to think aloud while solving five problems demanding an analysis of data organized in tables. The students were then interviewed. Results indicated that (1) students based their conclusions on only part of the data; (2) students did not use either efficient or sufficient visual representations; (3) students did not apply mathematical operations efficiently; and (4) students referred to or built a context to the problem. The results of the current research may assist educators to design efficient curricula while being aware of and taking into account (1) students' difficulties in employing previously learned mathematical devices to analyze data, (2) students' skills in choosing appropriate and efficient visual representations to present and interpret the data, and (3) strategies employed by students in analyzing multivariate data.     

Ninth graders' attitudes towards selected uses of technology

Technology has become affordable and available for science teachers of all grade levels. This study presents the results of collecting attitudinal data from over 240 ninth grade physical science students prior to the integration of technology into their science curriculum. The data were evaluated utilizing a probabilistic model, which corrects for the nonlinearity of rating scale responses. Results indicate that surveyed students, on average, were supportive of the integration of a wide range of technology into their classroom. Although students were generally positive, they were statistically more supportive of activities that involved the sending of electronic messages to other students when a comparison was made to their attitudes toward the collecting of data with computer probes. The results suggest that physical science teachers may want to initiate the integration of technology into classes by first requiring students to use electronic mail to share data collected without the aid of a computer. This activity could, in turn, be followed by labs that require the collecting of data with computer probes and subsequent sharing of that data with other students through electronic mail.     

Grading Standards,Student Ability and Errors in College Admission

Grades are important for admission of students in most higher education programs. Analyzing admission and student performance data at a major Norwegian business school, we find that the grading practice of teachers at regional colleges sending students to the school is affected by the average performance of the students being graded. Teachers at colleges recruiting good students from upper secondary school tend to be strict in their grading practice, while teachers at colleges recruiting less good students tend to follow a lenient practice. This has implications for the interpretation of grades and hence for optimal admission procedures. We develop a methodology to assess the consequences of differential grading standards. Approximately 10% of the students in our data are admitted at the expense of more competent students. We demonstrate costs for the school admitting the wrong students and in particular for the rejected students.     

中国科大研究生与全国科教人员体质比较研究

本应用统计法和比较法，分析了中国科大研究生的体质现状，揭示研究生体育工作中存在的问题，为研究生体育锻炼和学校体育工作的决策提供科学的指导和依据，为相关部门提供有关重要参考价值的基础数据。     

Students' approaches to exploring relationships between categorical variables

In the context of an afterschool program in which students explore relatively large authentic datasets, we investigated how 11- to 14-year old students worked with categorical variables. During the program, students learned to use the Common Online Data Analysis Platform (CODAP), a statistical analysis platform specifically designed for middle and high school students, to create and interpret graphs. Following the program, we conducted individual clinical interviews, during which students used CODAP to answer questions about relationships between variables. Here, we describe how students engaged in exploratory data analysis that involved looking at relationships between two categorical variables. Students worked from data in table form and created “contingency graphs,” a variant of contingency tables, which they used to analyze and draw insights from the data. Our research identified four strategies that students used to examine the data in order to explore patterns, make comparisons, and answer questions with the data.     

Student absence in South Australian schools

Students who are frequently absent from school (‘non-attenders’) are considered to be ‘at risk’. State education departments have recently begun to improve their collection of student absence data, often to contribute to the development of performance standards for schools. This paper presents a summary of data from the Term 2 collection in South Australian government schools. The data were combined with student information, which allowed examination and comparison of attendance patterns for different groups of students. The author provides details about which students were absent in those years, the frequency of students’ absences, which students had no absences, and the stated reasons for students’ absences. The author recommends that no single attendance standard can be set for schools, based on the differential targets shown for different groups of students.     

University students with learning disabilities: Results of a national survey

Abstract

Despite increasing attention to disadvantaged students in Australian universities, data on students with disabilities is sparse. This paper reports the results of a 1996 national survey on students with learning disabilities (LD) in Australian universities. The data collected from disability support staff indicate that in 1996 they supported approximately 1000 students with LD: this group of students comprised 12% of all supported students with disabilities. The gender ratio of supported students with LD did not differ significantly from that of all university students, but the supported students with LD were more likely to be studying part‐time, less likely to be studying for a higher degree, and less likely to be aged under 20 years.     

大数据视域下高校学生精准资助的模式探究

高校学生精准资助是我国精准扶贫政策在高校领域的有效延伸,它能够提升高校扶贫工作的针对性、目的性和精准化。当前,在“互联网+”背景下,借助大数据技术推进贫困生精准资助工作成为高校学生精准资助的崭新模式。通过对大数据的科学内涵、基本价值、大数据助推高校精准资助工作的必要性以及当前大数据视域下高校学生精准资助工作存在的问题进行客观准确分析,提出了大数据视域下高校学生精准资助模式建构的有效路径。     

基于核心素养的小学生数据分析观念培养研究

数据分析观念是统计与概率领域重点培养的核心素养之一。小学数学统计教学中,教师可以从学生数据分析的内在需求、数据分析所需要的基本统计量的理解等方面入手,通过开展探究性、生活性和选择性活动,让学生体会数据分析的价值,帮助学生用统计的眼光看待生活、用数据分析的策略解决问题,从而发展学生数据分析能力,提升学生核心素养。     

Effect of emotion-aware interventions on students’ behavioral and emotional states

The goal of this research is to investigate the effect of emotion-aware interventions on students’ behavioral and emotional states. To this end, we collected data from 12 students in the 9th grade in a high school in Turkey. The data collection took place in two sessions of an English Course. While the students were reading articles and solving relevant questions, our data collection application running in the background recorded the videos of the individual students through a camera and captured students’ screens in a non-intrusive manner. In total, we had 12.5 h of student data. We employed the human expert labeling process (HELP) (Aslan et al. in Workshop proceedings at international conference on intelligent tutoring systems (ITS), pp 156–165, 2016) to have the data labeled (150 h of data labeling in total). The data collection application was designed in a way that it also collected emotional self-labels (i.e., emotional states as self-reported by students at any time of learning). We leveraged emotional self-label information to suggest various real-time interventions for the students. The results obtained using the final expert labels showed that the percentage of the students’ Satisfied state was significantly higher after interventions. The results also demonstrated that although the interventions were triggered by the emotional states as self-labeled by the students and tailored to improve such states, there was a major positive impact of these interventions on students’ behavioral states. This preliminary study showed that even with a limited set of emotion-aware interventions based on self-labels, students’ states could be impacted positively. Conducting large-scale pilots leveraging more advanced interventions is a future direction for our research.     

The effectiveness of learning analytics for identifying at-risk students in higher education

Abstract

In this article we investigate the effectiveness of learning analytics for identifying at-risk students in higher education institutions using data output from an in-situ learning analytics platform. Amongst other things, the platform generates ‘no-engagement’ alerts if students have not engaged with any of the data sources measured for 14 consecutive days. We tested the relationship between these alerts and student outcomes for two cohorts of first-year undergraduate students. We also compared the efficiency of using these alerts to identify students at risk of poorer outcomes with the efficiency of using demographic data, using widening participation status as a case study example. The no-engagement alerts were found to be more efficient at spotting students not progressing and not attaining than demographic data. In order to investigate the efficacy of learning analytics for addressing differential student outcomes for disadvantaged groups, the team also analysed the likelihood of students with widening participation status generating alerts compared with their non-widening participation counterparts. The odds of students with widening participation status generating an alert were on average 43% higher, demonstrating the potential of such a system to preferentially target support at disadvantaged groups without needing to target directly based on immutable factors such as their socio-economic background.     

Web-Based Student Assessment for Program Evaluation

As part of an overall evaluation of the Global Learning and Observations to Benefit the Environment, (GLOBE) program, we designed a Web-based assessment environment to measure students' environmental awareness and data analysis skill. It was expected that students who were identified as high implementers in the GLOBE program would outperform low implementers in their ability to construct environmental inferences and the degree to which they could analyze environmental data. Seven high and middle school classrooms were identified as either high or low GLOBE implementers depending on the amount of atmospheric data they had collected during the year. Within each classroom students were assigned into smaller learning groups of three students per group. A total of 32 groups participated in this study. Analysis of students' responses to the tasks revealed that the students differed in their performance. Overall, the results showed that students in the high implementing classrooms were more likely to construct higher-level environmental inferences than students in the low implementing classes. Contrary to expectations, middle school students were more likely than high school students to solve the data analysis problem correctly. However, upon further analyses, high school students constructed more data graphs and were more skilled in providing correct evidence to support their decision making than were middle school students in GLOBE. This study confirms the viability of using technology-based assessments for measuring students' environmental awareness and data analysis.     

Examination of Designed Materials in Digital Environment on Concept Teaching in Special Education

ABSTRACT

The aim of this study is to investigate the effectiveness of digital materials designed for the use of teachers and students in teaching concept skills to students in need of special education. The concept of the long term was included in the study. As a research model, a single-subject research model and a multiple-probe model were used. The participants of the study consisted of 4 students from different disability groups with the appropriate sampling method. In the study, data forms prepared by the researcher were used as data collection tools. According to the data obtained from the data forms, it was observed that 4 students who participated in the study learned the concept in different time intervals by using digital materials. In general terms, it was observed that all of the students learned the long concept, which is the concept to be taught. By using digital materials, it is concluded that students who are in need of special education support students’ individual learning skills independently of time and place and this was supported by teachers’ opinions.     

Exploring statistical inference for population means through rubber chickens

Obtaining relevant data and conveying limitations of the results are two integral components to a successful statistical analysis. It is difficult for students to internalize a deep understanding of these components using only curated, textbook‐style examples. Through hands‐on data collection, this activity provides a channel for students to experience all steps, from start to finish, of a data analysis for means with paired data. Students are able to experience the challenges associated with good data collection practices and relate these practices to the underlying methodological assumptions.     

Schools for the deaf and the No Child Left Behind Act

The No Child Left Behind Act of 2001 (NCLB) emphasizes educational accountability for all students. Twenty-eight states have policies to aggregate student participation and proficiency data for schools for the deaf in NCLB reports. The remaining states account for these students in other ways: referring student data to "sending" schools and aggregating data to the district or state level are most prominent. In reports of student assessment results for academic year 2002-2003, three schools for the deaf made "Adequate Yearly Progress" under NCLB: These schools demonstrated at least a 95% participation rate in assessments, and at least 95% of their students met or surpassed state proficiency benchmarks in reading and mathematics. Proficiency levels for other schools varied by report, but were often comparable to those of students with disabilities. Challenges and strategies for capturing the impact of NCLB accountability policies on deaf students are discussed.     

The Stanford Achievement Test, 9th Edition: National Norming and Performance Standards for Deaf and Hard-of-Hearing Students

This article presents a rich context of information for interpreting Stanford Achievement Test scores and for describing the achievement of deaf and hard-of-hearing students. The publisher's national norming of the Stanford Achievement Test provides a context of actual performance of hearing students. The publisher's Performance Standards provide a context of expectations for hearing students as determined by a panel of experts. The Gallaudet Research Institute's norming of the test on a national sample of deaf and hard-of-hearing students provides a context of test performance by this special population. A smaller subsample of the deaf and hard-of-hearing students who take the same test levels as hearing students provides an additional reference group with respect to the Performance Standards. Information from these sources is brought together into two graphical contexts to address these questions: Can the normative data from the publisher's national standardization of the test with hearing students, and the normative data from the GRI's national norming of the test with deaf and hard-of-hearing students provide a useful context for the interpretation of individual test scores? Can they provide a useful way to examine achievement of groups of students? Can the new Performance Standards defined by the test publisher offer a useful context for test score interpretation for high-achieving deaf and hard-of-hearing students?     

Modeling Transport in Streams

Abstract

We present our experience with an open-ended lab activity that we designed for students with a Calculus 1 background. With the goal of learning how scientists study transport in streams, the students collected data on how a pulse of leaves travels down a nearby stream. Students who had little to no experience with data with two independent variables (distance downstream and time) graphed the data and developed a first, partial differential equations model. We took the students through a second cycle of the modeling process to improve the model to account for leaves that got stuck to the bottom or sides of the stream. Given the appropriate, inquiry-based pedagogy, our lab maintains high levels of cognitive demand and introduces students to the modeling process.     

利用Matlab软件评价学生分析化学实验数据

在分析化学实验中,评价学生的实验成绩,传统的方法是用教师预先所做的几个测量数据作为标准,这样给学生的数据评分是不完全合理的。文章以学生测定水的硬度实验为例,应用Matlab软件编制程序对学生的数据进行分析,能快速、简便、直观地绘制出相应学生实验数据分布的图形,这样就能客观、公正地评价学生的实验数据。