Carole R. Beal

Tactical Language Training System: An Interim Report

Tactical Language Training System helps learners acquire basic communicative skills in foreign languages and cultures. Learners practice their communication skills in a simulated village, where they must develop rapport with the local people, who in turn will help them accomplish missions such as post-war reconstruction. Each learner is accompanied by a virtual aide who can provide assistance and guidance if needed, tailored to each learner’s individual skills. The aide can also act as a virtual tutor as part of an intelligent tutoring system, giving the learners feedback on their performance. Learners communicate via a multimodal interface, which permits them to speak and choose gestures on behalf of their character in the simulation. The system employs video game technologies and design techniques, in order to motivate and engage learners. A version for Levantine Arabic has been developed, and versions for other languages are in the process of being developed.

Reading Proficiency and Mathematics Problem Solving by High School English Language Learners

The study focused on the relationship of English proficiency and math performance in a sample of high school students, including 47% English language learners (ELLs). Data sources included state math test scores, study-specific pre- and posttest scores, problem solving in an online math tutorial, and responses to a self-report assessment of mathematics self-concept. English conversational and reading proficiency data were available for the ELLs. Results indicated that math performance for the ELLs increased with English-reading proficiency in a nonlinear manner. ELLs’ English-reading proficiency predicted math test scores, progress in the online math tutorial, and math self-concept.

Temporal Data Mining for Educational Applications

Intelligent tutoring systems (ITSs) acquire rich data about studentsO behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.

The Relationship Between Mathematics and Language: Academic Implications for Children With Specific Language Impairment and English Language Learners

PURPOSE The present study examined the relationship between mathematics and language to better understand the nature of the deficit and the academic implications associated with specific language impairment (SLI) and academic implications for English language learners (ELLs). METHOD School-age children (N = 61; 20 SLI, 20 ELL, 21 native monolingual English [NE]) were assessed using a norm-referenced mathematics instrument and 3 experimental computer-based mathematics games that varied in language demands. Group means were compared with analyses of variance. RESULTS The ELL group was less accurate than the NE group only when tasks were language heavy. In contrast, the group with SLI was less accurate than the groups with NE and ELLs on language-heavy tasks and some language-light tasks. Specifically, the group with SLI was less accurate on tasks that involved comparing numerical symbols and using visual working memory for patterns. However, there were no group differences between children with SLI and peers without SLI on language-light mathematics tasks that involved visual working memory for numerical symbols. CONCLUSION Mathematical difficulties of children who are ELLs appear to be related to the language demands of mathematics tasks. In contrast, children with SLI appear to have difficulty with mathematics tasks because of linguistic as well as nonlinguistic processing constraints.

Assessing Students’ Problem Solving Ability and Cognitive Regulation with Learning Trajectories

Learning trajectories have been developed for 1650 students who solved a series of online chemistry problem solving simulations using quantitative measures of the efficiency and the effectiveness of their problem solving approaches. These analyses showed that the poorer problem solvers, as determined by item response theory analysis, were modifying their strategic efficiency as rapidly as the better students, but did not converge on effective outcomes. This trend was also observed at the classroom level with the more successful classes simultaneously improving both their problem solving efficiency and effectiveness. A strong teacher effect was observed, with multiple classes of the same teacher showing consistently high or low problem solving performance.

AnimalWatch: An Intelligent Tutoring System for Algebra Readiness

The AnimalWatch tutoring system provides students with instruction in algebra readiness problem solving, including basic computation, fractions, variables and expressions, basic statistics and simple geometry. Students solve word problems that include authentic environmental science content, and can access a range of multimedia resources that provide instructional scaffolding, such as video lessons and worked examples. Because providing learners with choices is associated with enhanced motivation, AnimalWatch is designed to allow students to decide what science topic they would like to learn about, and when they would like to navigate between different modules in the system. Several evaluation studies in classroom settings have found positive effects of AnimalWatch on study-specific measures of problem solving. Benefits have been strongest for students who are struggling in math, suggesting that technology-based learning can be especially effective for this population.

Human Computation as an Educational Opportunity

“Citizen science” refers to the emerging practice in which individuals in the community, often en masse, partner with researchers to assist with data collection, analysis or interpretation. Such partnerships benefit researchers through access to data at a scale not possible for individuals or small teams. To date, the benefits to the citizen scientists have been less apparent, although some have argued that participation increases critical thinking and appreciation for science methodologies. The present chapter reports a case study in which 12-year-old citizen scientists contributed to a major research investigation of evapotranspiration and, in turn, deepened their own understanding of the water cycle.