Language Proficiency and Cultural Intelligence in Distance English-Language Learning

The purpose of this study is to explore the viability of an international distance English-language program in the development of language and cultural proficiency. Students participated in tests at the beginning and at the end of the course to determine how well they developed both language and cultural proficiencies. The measures included (a) the computer-administered Oral Proficiency Interview (OPIc) from theAmerican Council on the Teaching of Foreign Languages (ACTFL), (b) ACTFL-aligned assessments of reading, listening, grammar, and vocabulary skills, and (c) the Cultural Intelligence Scale (CQS). In addition, course activities surveys provided additional information about student perceptions of course activities. Participants in this study came from various countries as they prepared to attend a U.S. university in Hawaii.The distance learning program fostered language proficiency through various learning activities, with an emphasis on synchronous dialogue over video chat technologies. In addition to English-language proficiency, the program sought to help students learn to effectively communicate with students from other cultures. Cross-cultural proficiency was fostered through cross-cultural dialogue with tutors, teachers, and other students. Students showed improvement in speaking, listening, vocabulary, and grammar. However, on average, students did not show an improvement in reading proficiency. Students reported that dialogue with tutors and teachers was among the most helpful activities in learning English. Students showed some improvement in cultural proficiency. However, this improvement was not universal across all measures of cultural proficiency. Students reported that certain activities—particularly dialogue with tutors and other students—as helpful in developing cross-cultural proficiencies.This study also investigated the relationship between language proficiency and cultural proficiency. Results were mixed. With a few exceptions, cultural proficiency did not predict a student’s language proficiency at the beginning of the course, during the course, or at the end of the course

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

An Intelligent Tutoring System for Learning TOEFL

An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields. In this paper, we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool

INSTRUCTIONAL BEHAVIORS AND STUDENT READING OUTCOMES IN A SCRIPTED TIER 2 INTERVENTION FOR FOURTH GRADE STRUGGLING READERS

This study examined instructors' spontaneous responses to student errors in a scripted Tier 2 reading intervention and the relationship between tutor responses and student comprehension outcomes. A sequential exploratory mixed-methods design was used to identify the types of behaviors tutors exhibited in response to student errors, using transcripts of lessons. Tutors used four types of off-script behaviors when reacting to students: (a) scaffolding, (b) telling, (c) unclear feedback, and (d) erroneous feedback. Differences in how tutors implemented the standard protocol were analyzed qualitatively and described using frequency counts. Tutors exhibited differences in the frequencies of each of the behaviors, and differed in how closely they adhered to scripted lesson. Although tutors overall exhibited 76.3% fidelity of implementation, certain components of the lessons were frequently omitted -modeling of the strategy, describing the purpose of the strategy, and providing opportunities for practice. These omissions may have influenced overall responsiveness for students receiving intervention. To determine how tutor differences might influence student outcomes, the frequency counts of the four spontaneous tutor behaviors were entered into regression equations to predict posttest scores on three measures of reading comprehension -Maze, Gates MacGinitie, and ASKIT. Findings indicate that scaffolding was related to student growth on one curriculum based measure of reading comprehension. The other three behaviors -telling, unclear feedback, and erroneous feedback -were not significantly related to student outcomes. Limitations, in light of these findings are considered. Implications for planning intervention studies and tutor training, as well as future directions for research, are discussed

Predicting Math Success in an Online Tutoring System Using Language Data and Click-Stream Variables: A Longitudinal Analysis

Previous studies have demonstrated strong links between students\u27 linguistic knowledge, their affective language patterns and their success in math. Other studies have shown that demographic and click-stream variables in online learning environments are important predictors of math success. This study builds on this research in two ways. First, it combines linguistics and click-stream variables along with demographic information to increase prediction rates for math success. Second, it examines how random variance, as found in repeated participant data, can explain math success beyond linguistic, demographic, and click-stream variables. The findings indicate that linguistic, demographic, and click-stream factors explained about 14% of the variance in math scores. These variables mixed with random factors explained about 44% of the variance

ALEKS Constructs as Predictors of High School Mathematics Achievement for Struggling Students

Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners\u27 performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics

Perceived parenting and social support: can they predict academic achievement in Argentinean college students?

The aim of this study was to test the ability to predict academic achievement through the perception of parenting and social support in a sample of 354 Argentinean college students. Their mean age was 23.50 years (standard deviation =2.62 years) and most of them (83.3%) were females. As a prerequisite for admission to college, students are required to pass a series of mandatory core classes and are expected to complete them in two semesters. Delay in completing the curriculum is considered low academic achievement. Parenting was assessed taking into account the mother and the father and considering two dimensions: responsiveness and demandingness. Perceived social support was analyzed considering four sources: parents, teachers, classmates, and best friend or boyfriend/girlfriend. Path analysis showed that, as hypothesized, responsiveness had a positive indirect effect on the perception of social support and enhanced achievement. Demandingness had a different effect in the case of the mother as compared to the father. In the mother model, demandingness had a positive direct effect on achievement. In the case of the father, however, the effect of demandingness had a negative and indirect impact on the perception of social support. Teachers were the only source of perceived social support that significantly predicted achievement. The pathway that belongs to teachers as a source of support was positive and direct. Implications for possible interventions are discussed.Fil: de la Iglesia, Guadalupe. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Freiberg Hoffmann, Agustín. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

ITS for Teaching TOEFL

Abstract: An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields .In this paper we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool