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Tools for Improving Assessment through Real Time Data Collection


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1 A collective intelligence application is one that harnesses the knowledge and work of its users to provide the data for the application and to improve its usefulness. The most hyped examples of collective intelligence applications have been labeled as "Web 2.0" applications. Web 2.0 is an amorphous term used to define a computing paradigm that uses the Web as the application platform and facilitates collaboration and information sharing between users [O'Reilly 2005]. Classic examples of Web 2.0 applications include: • Wikis, collective Web sites that allow users to add, edit, and delete content; • Blogs, a Web journal that lets users post news or comments; • Social network services help build and verify online social communities that share common interests; and • Social bookmarking allows users to share bookmark for websites and organize them with tags. Web 2.0 sites are database-driven and are considered to be "infoware," in that, they are data intensive and the more data they contain the more valuable they become [McFedries 2007]. Much of what has been published on Web 2.0 to date has focused on using these new Web 2.0 tools in innovative ways. For example, in early 2005 Motorola created a corporate collaboration infrastructure which includes instant messages (12 million per day) and blogs (2,600 corporate wide), and wikis (3,200 corporate wide). After 18 months of use, Motorola had Motorola's collaboration infrastructure contained 17TB of searchable data [Gibson 2006]. Microsoft and IBM are linking Web 2.0 capabilities with enterprise software. These new tools will allow companies to manipulate and tag their data and to create internal social networks and virtual teams. Collective intelligence is a fundamentally different way of viewing how applications can support human interaction and decision making. Most pre-Web 2.0 applications have focused in improving the productivity or decision making of the individual user. The emphasis has been on providing the tools and data necessary to fulfill a specific job function. Under the collective intelligence paradigm, the focus is on harnessing the intelligence of groups of people to enable greater productivity and better decisions than are possible by individuals working in isolation.
Behavior Chart The final type of tagging available in DDtrac is the semantic tagging of the narrative comments taken as a part of the daily instructional, behavior or socialization observation notes taken by practitioners. These semantic tags closely resemble the tags common on many Web 2.0 sites (e.g. Flickr, Delicious, Blogger etc.). They are freely chosen keywords which allow for overlapping associations and that can be used for later retrieval and analysis of specific comments. For example, a student may exhibit a finger flicking behavior infrequently. The practitioner might note this in the daily notes along with other observations. Then, if the behavior becomes a problem, the practitioner could retrieve all of the comments tagged “flicking” to look for any patterns. Goals & Objectives Wiki. The education programs of developmentally disabled children are defined in an Individual Education Program (IEP), which establishes long-term goals and short-term objectives tailored to the needs of the individual student [Wright et. al. 2007]. The IEP also includes descriptions of the student’s current level of performance, strengths, and individual needs. In most schools this document includes input from several different people including the special education teacher, the regular education teacher, the therapy specialists, the student’s parents and external advocates. The IEP is an important document because it defines the direction for treatment to be taken for the upcoming year.
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Documenting change in young children receiving special education services requires the appropriate use of data collection methods. Asking the right questions and matching the data collection procedures to the specific question is at the heart of successful documentation. The task is not an easy one for practitioners. The nature of early intervention, including a holistic view of the child and the move toward delivery of services in natural settings, often makes the task appear impossible, or at least impractical. This article proposes methods for asking analytic and systemic questions and using quantitative and qualitative data for documenting child behavior change. The goal is to develop a portrait of the child, capturing a full view of change in context. Suggestions are provided for developing data collection protocols that are valid but that are also manageable in practice.
Curriculum-based measurement (CBM) is an approach for assessing the growth of students in basic skills that originated uniquely in special education. A substantial research literature has developed to demonstrate that CBM can be used effectively to gather student performance data to support a wide range of educational decisions. Those decisions include screening to identify, evaluating prereferral interventions, determining eligibility for and placement in remedial and special education programs, formatively evaluating instruction, and evaluating reintegration and inclusion of students in mainstream programs. Beyond those fundamental uses of CBM, recent research has been conducted on using CBM to predict success in high-stakes assessment, to measure growth in content areas in secondary school programs, and to assess growth in early childhood programs. In this article, best practices in CBM are described and empirical support for those practices is identified. Illustrations of the successful uses of CBM to improve educational decision making are provided.
The importance of using data collection and analysis procedures to monitor academic and social behavior progress of students with emotional and behavioral disorders is the focus of this article. After reviewing literature supporting the efficacy of data collection procedures used in classrooms for students with disabilities, issues regarding the ease of implementation are addressed. Literature reviewing ways in which modifications can be made to data collection procedures to enhance their use in classrooms are presented.
This paper was the first initiative to try to define Web2.0 and understand its implications for the next generation of software, looking at both design patterns and business modes. Web 2.0 is the network as platform, spanning all connected devices; Web 2.0 applications are those that make the most of the intrinsic advantages of that platform: delivering software as a continually-updated service that gets better the more people use it, consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others, creating network effects through an "architecture of participation," and going beyond the page metaphor of Web 1.0 to deliver rich user experiences.
Autism is a serious psychological disorder with onset in early childhood. Autistic children show minimal emotional attachment, absent or abnormal speech, retarded IQ, ritualistic behaviors, aggression, and self-injury. The prognosis is very poor, and medical therapies have not proven effective. This article reports the results of behavior modification treatment for two groups of similarly constituted, young autistic children. Follow-up data from an intensive, long-term experimental treatment group ( n = 19) showed that 47% achieved normal intellectual and educational functioning, with normal-range IQ scores and successful first grade performance in public schools. Another 40% were mildly retarded and assigned to special classes for the language delayed, and only 10% were profoundly retarded and assigned to classes for the autistic/retarded. In contrast, only 2% of the control-group children ( n = 40) achieved normal educational and intellectual functioning; 45% were mildly retarded and placed in language-delayed classes, and 53% were severely retarded and placed in autistic/retarded classes. (31 ref)
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