Preston Lewis and his research team at the University of Kentucky are investigating patterns of student use of digital text with Read & Write GOLD (Texthelp Systems) and the relationship of these patterns to individual learner characteristics and performance.
Procedure:
Investigate patterns of student use in Read & Write Gold, (Texthelp, Inc.) by examining (a) student setting preferences (e.g., voice selection, voice speed, dual color highlighting) and (b) logs of student use (e.g., frequency/duration of use, dictionary, word prediction, calculator access, spelling etc.). Compare patterns of student use to determine their possible relationship to disability and other learner characteristics/demographics.
Project Overview :
NCSeT studies of interest include examination of how students use digital text and also the relation of its use to student performance. Kentucky is in a unique position to examine these issues as a result of the statewide proliferation and availability of software (e.g., text readers) and core content in a digital format. To date, approximately 90% of KY schools have acquired a site license for Read and Write Gold software from Texthelp, Inc. This software provides electronic learning and literacy tools for use by struggling learners. Read & Write Gold can be installed by schools using individual student user profiles. This allows each student’s preferences to be readily available to them when they start up the software without having to re-set them every time they boot up the software.
The tools include text to speech, speaking spell check, speaking dictionary, speaking calculator and word prediction. Students can also set preferences for how text is displayed on screen, such as setting font size, color of the text and background. Students also have an array of electronic voices to choose from and can vary the speed at which the voice reads selected text.
The NCSeT KY ETEXT Study is interested in examining if there are patterns of student display preferences (e.g., text/background colors, voice engine/speed selections, etc,) that may be related to student characteristics, such as type of disability. There is also interest in comparing digital text usage patterns (i.e., frequency/duration of software use) to student performance on state assessment.
The software manufacturer, Texthelp, Inc., has developed a program that can be used by local school district network administrators to extract individual student preference (i.e., User settings) and usage (i.e., Activity) data (e.g., frequency/duration of software use) from their local workstations. A record of frequency and type of spelling errors is also provided (i.e., Spelling Log). This data extract can then be downloaded into a local data base (e.g., Access). The usage data can then be examined to evaluate how often and for what durations the software is being used instructionally. The preference data can be examined to determine patterns of student settings that might be related to age, grade or disability or learning style. The spelling log is valuable for learning what are the most frequent type of spelling errors (e.g., phonetics, missing letters, consonant sounds, etc.). This can offer direction in individualizing future spelling instruction.
The data extraction program from Texthelp is also designed to remove all student personal identifiers (e.g., student login) and have them replaced by school staff with non-identifiable student numbers (SSIDs or State Student Identification Numbers). These data files will then be provided by each participating school electronically to the Kentucky Department of Education (KDE) so that the KDE can use the SSID numbers to cross reference the RWG digital text usage data with the state test performance data (e.g., reading & math scores) for each student. These SSID numbers can then also be cross referenced to a state demographics file with associated individual student characteristics (e.g., disability, age). The KDE will then provide the software usage data, the student state test performance data, and the student demographic data (i.e., disability, age) to staff at HDI/UK to analyze. Analysis will be for any correlations of patterns of usage (user setting preferences) to student demographics and software frequency/duration usage data to state test performance. Some of the RWG digital text student users in KY are those without disabilities, which will offer other demographics for use in our analysis and comparison to the group of students with disabilities. (e.g., Title I students, Limited English Proficiency, Free or Reduced Lunch, etc).