Reading Tutors’ Interpretation of Curriculum-Based Measurement Graphs

This study examined reading tutors’ interpretation of reading progress-monitoring graphs. A think-aloud procedure was used to evaluate tutors at two points in time, before and after a year of service as an AmeriCorps reading tutor. During their service, the reading tutors received extensive training and ongoing coaching. Descriptive results showed a positive change from the Time 1–think-aloud (pretest) to the Time 2–think aloud (posttest). There were statistically significant changes from Time 1 to Time 2 for the majority of graph interpretation variables measured. Data suggest that the right type of support and training may serve to enable reading tutors to develop the skills to contribute to data-based decision-making within multitiered systems.

A Reading Tutor for Low-Literacy Adults

According to the Organization for Economic Cooperation and Development (OECD), the mean proficiency in literacy among adults in the US and Canada is at Level 2. Adults at this level cannot process dense texts, eliminate irrelevant information, perform multi-step operations, or evaluate the reliability of a source. The Reading Tutor is a website that was created to help low-literacy adults improve their English. It will be free to use the website that is personalized to the literacy level of every user. Creating a website allows people to increase their literacy levels without facing the stigma that comes with attending a class in person. Adults are inclined to improve their English because it often affects their career potential, socio-economic status, and health. The Reading Tutor has two major components: the passages and the scenarios. Passages are stories that the user can read and answer questions about. Scenarios are plots with questions that the user must answer to move on. In recent work, the information for each scenario was organized into spreadsheets to simplify the process of entering data into the code. The system architecture consists of HTML, CSS, Javascript, MySQL, Python, and Django. The newest development in this project was the improvement of the user intake experience. Before starting the passages and scenarios, the website collects information from each adult. The user "interests" pages are the latest additions to the site and these pages ask about the user’s hobbies. That data will then be used to incorporate their interests into later questions. It was important to add this feature to the website because relevance is a motivator for the user demographic. The next steps for the website are to log the user’s interests into the database. Future enhancements also include the creation of more scenarios to accommodate to the different user interests.

Generating Reliable Feedback About Students’ Performances Within an Automated Reading Tutor

Personalized learning technologies such as automated reading tutors have been proposed as a way to help struggling readers acquire needed skills while simultaneously encouraging engaged, sustained reading of entire books. This article investigates a key step in the development of such technologies: translating an entire novel into a sequence of nonoverlapping reading passages, so that valid feedback about students’ performances can be generated at the end of each tutoring session. A critical reliability issue that may be introduced at this step is identified, an approach for addressing that issue is proposed, and an evaluation conducted with respect to three popular middle-grade novels is presented. The findings suggest that our new approach provides more reliable evidence about students’ abilities and thus may be more appropriate for use when building automated reading tutors.

Speech Recognition Software Contributes to Reading Development for Young Learners of English

Thirty-six English language learners aged 6;8 to 12;6 years received practice with The Reading Tutor, which uses speech recognition to listen to oral reading and provides context-sensitive feedback. A crossover research design controlled effects of classroom instruction. The first subgroup worked with the software for 3.5 months, and following a week's crossover period, the second subgroup worked for a subsequent 3.5 months. Both groups were assessed to obtain comparable gains both in regular classroom with English as an Additional Language (EAL) support and in the classroom condition with EAL support plus the Reading Tutor. Oral reading fluency was assessed by the DIBELS measure. Fluency was also calculated by the program, and grade level of materials mastered was assessed by the software's logs. Both groups made significant gains in oral reading fluency and grade level of materials mastered, according to measures internal to the software. For one period, gains in fluency following experience with the program appeared to have been slightly larger than gains with regular classroom instruction and EAL support only.