A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations

International audienceOnline planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally and greedily but that are computationally more efficient while requiring a simpler learner model. Even with the availability of data from several tutoring systems, it is never possible to have a highly accurate student model or one that is tuned for each particular student. We study what is the impact of the quality of the student model on the final results obtained with the two algorithms. Our hypothesis is that the higher flexibility of multi-armed bandits in terms of the complexity and precision of the student model will compensate for the lack of longer term planning featured in POMDPs. We present several simulated results showing the limits and robustness of each approach and a comparison of heterogeneous populations of students

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

Exploring the effectiveness of Self-management Programs for Students with Disruptive Behaviors: A Comprehensive Literature Review

Disruptive behaviors exhibited by children and youth pose a major problem for students exhibiting the behaviors, their peers, parents, and teachers. Disruptive behaviors including shouting, aggression, off-task behaviors, and noncompliance, correlate with poor social skills, low peer acceptance, higher rates of academic deficiencies, and in adulthood instability in relationships and employment. Self-management programs employ traditional behavior management methods and with self-management components to teach students to self-monitor or evaluate their behavior. By teaching students to be aware of and to manage their own behavior students may be better able to generalize appropriate behaviors to other less supervised settings, complete more work, and experience a sense of accomplishment for controlling their behavior. Past reviews of self-management literature have demonstrated the effectiveness of self-management interventions in changing disruptive behaviors and documented limitations of the research. However, the most recent review was completed over a decade ago, therefore a current review of self-management programs for disruptive behaviors was completed. The review indicated that recent literature has corrected some past limitations by studying a larger variety of age groups in different settings and by providing replicable intervention steps. Unfortunately, many variables continue to be neglected (e.g., generalization, maintenance, social validity, treatment integrity). The strengths and limitations of current self-management literature as applied to changing disruptive behaviors exhibited by youth are discussed and areas for future research recommended

Different Faces in Our Classrooms: Teachers\u27 Cultural Perspectives of Heterogeneous School Environments

The foundation of the United States’ educational system is that all students will be educated equally by offering access to knowledge, opportunities, and services resulting in the creation of positive societal contributors. However, this task is complex and challenging. Heterogeneous student populations due to increased culturally diversity, do not match the stable teacher population. Therefore, a disconnection occurs between student and teacher populations and corresponding cultural views. Adopting cultural theories from anthropology, business, and education, this quasi-ethnographic study, within one elementary school, examined the relationship between the created culture and understandings of various sociocultural student identities by three first grade teachers and how, if at all, their classroom practices afforded any student academic advantages or disadvantages. Presented are teachers’ personal interpretations about various cultures and how these constructs formed beliefs, attitudes, and values about student capital, teaching, educational practices, judgments, and accurate allocation of resources (entitlements) for their students. Study findings indicate an acute participant awareness of cultural differences between themselves and their students when allocating appropriate academic opportunities and services. A shift in the teaching profession from one that imparts knowledge toward a focus on meeting physical and emotional needs of students, aligned with Maslow’s Hierarchy of Needs theory. The emphasis on standardized assessments and their effect on classroom teaching methods and strategies, has created a cultural lag between classrooms and school district and state mandates. Addressing these challenges portends teachers become transformative learners and engage in culturally responsive teaching practices

Putting the Horse Before the Cart: Utilizing What Assessment Data Reveal About Struggling Young Adolescent Readers to Inform Policy and Instruction

In recent years, increased attention has been paid to accelerating the development of struggling young adolescents’ reading skills (Franzak, 2006). It has been widely acknowledged that these students require intensive instruction in reading in order to meet changing societal demands (Allington, 2002; Afflerbach, 2004; Alvermann, 2001; Biancarosa & Snow, 2004). Score reporting from the National Assessment of Educational Progress (NAEP) may demonstrate a dichotomy amongst our young adolescent readers, but the scores do not tell us about the specific needs of individual students. In other words, these levels essentially create two groups: those who can read and those who cannot. Further, instructional decisions are being made based on the limited proficiency scores of state mandated standardized assessments. This method of reporting scores creates a notion of homogeneity amongst the reading skills of young adolescents. The purpose of this multivariate correlational study was to determine the patterns of reading abilities amongst struggling young adolescent readers in an attempt to demonstrate the heterogeneous nature of these students and the variability of reading skills they bring to middle school classrooms (grades 6-8), in an effort to influence both policy and instruction at this level. Data were collected during the 2005-2006 academic year. Each student participant (n=94) was administered five assessments that measured alphabetics (phonemic awareness and phonics), fluency, vocabulary, and comprehension, which were representative of both the highly and less constrained skills (Paris, 2005) presented as essential components of reading instruction by the National Reading Panel (NRP, 2000). Independent samples t-tests were used to compare the assessment means of several subgroups, students who qualified for special education, free and reduced price lunch, and English Language Learner services, and those who did not qualify for these services. Results indicated all of these students scored below grade level on the assessments administered. However, all of the students represented varying abilities and needs that required further analysis. Factor analysis was then utilized to determine which reading skills assessed were most directly related to student performance on TCAP. Three factors emerged, meaning, decoding, and rate and accuracy. Finally, cluster analysis presented four distinct clusters of struggling young adolescents, which represented heterogeneous abilities in various reading skills. Results indicated one-size-fits-all approaches to policy and instruction relating to struggling young adolescent readers do not meet the heterogeneous needs of this population of students. Rather, in-depth assessment and diagnoses are necessary to determine the most appropriate instructional tools for individual students. Further, by suggesting the use of state mandated standardized assessment scores be the sole indicator of student placement in remedial reading courses, policy fails to address the multifaceted process of reading and the differing trajectories of young adolescent reading development

Culturally Responsive School Leadership and Gifted Identification in a Heterogeneous School: A Case Study

The purpose of this qualitative descriptive single case study is to gain a deeper understanding of how school leaders approach giftedness identification in a school that serves students who are racially, ethnically, linguistically, and economically diverse through the lens of culturally responsive school leadership. This study utilizes descriptive case study methods to examine the screening of first-grade students using data collected over six weeks through interviews, a focus group, observations, journaling, and document reviews. Data analysis employs an iterative coding process aided by NVivo to discover themes and develop thick descriptions of the gifted identification process within this context. Study participants include staff members who directly and indirectly provide educational services for the screened students. The findings and conclusions of this study further the dialogue for helping school leaders improve teacher practice and build capacity for identifying giftedness in students from diverse backgrounds and cultures

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them