Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.

Writing to learn and learning to write across the disciplines: Peer-to-peer writing in introductory-level MOOCs

© Comer, Clark, Canelas.This study aimed to evaluate how peer-to-peer interactions through writing impact student learning in introductory-level massive open online courses (MOOCs) across disciplines. This article presents the results of a qualitative coding analysis of peer-to-peer interactions in two introductory level MOOCs: English Composition I: Achieving Expertise and Introduction to Chemistry. Results indicate that peer-to-peer interactions in writing through the forums and through peer assessment enhance learner understanding, link to course learning objectives, and generally contribute positively to the learning environment. Moreover, because forum interactions and peer review occur in written form, our research contributes to open distance learning (ODL) scholarship by highlighting the importance of writing to learn as a significant pedagogical practice that should be encouraged more in MOOCs across disciplines.

Peer rejection and social information-processing factors in the development of aggressive behavior problems in children.

The relation between social rejection and growth in antisocial behavior was investigated. In Study 1,259 boys and girls (34% African American) were followed from Grades 1 to 3 (ages 6-8 years) to Grades 5 to 7 (ages 10-12 years). Early peer rejection predicted growth in aggression. In Study 2,585 boys and girls (16% African American) were followed from kindergarten to Grade 3 (ages 5-8 years), and findings were replicated. Furthermore, early aggression moderated the effect of rejection, such that rejection exacerbated antisocial development only among children initially disposed toward aggression. In Study 3, social information-processing patterns measured in Study 1 were found to mediate partially the effect of early rejection on later aggression. In Study 4, processing patterns measured in Study 2 replicated the mediation effect. Findings are integrated into a recursive model of antisocial development.

Peer-Informed Learning on Increasing Contraceptive Knowledge Among Women in Rural Haiti

Contraceptive prevalence in Haiti remains low despite extensive foreign aid targeted at improving family planning. [1] Earlier studies have found that peer-informed learning have been successful in promoting sexual and reproductive health. [2-5] This pilot project was implemented as a three-month, community-based, educational intervention to assess the impact of peer education in increasing contraceptive knowledge among women in Fondwa, Haiti. Research investigators conducted contraceptive information trainings to pre-identified female leaders of existing women’s groups in Fondwa, who were recruited as peer educators (n=4). Later, these female leaders shared the knowledge from the training with the test participants in the women’s group (n=23) through an information session. Structured surveys measuring knowledge of contraceptives were conducted with all participants before the intervention began, at the end of the intervention, and four weeks after the intervention. The surveys measured general contraceptive knowledge, knowledge about eight selected types of modern contraceptives and contraceptive preferences and attitudes. Only test participants showed significant improvement in their general contraceptive knowledge score (p<0.001), but both test participants and peer educators showed significant improvement in overall knowledge scores for identifying the types and uses of modern contraceptive methods. Assessment for knowledge retention remained significantly higher four weeks after the intervention than prior to the intervention. Therefore, a one-time, three-hour peer-based educational intervention using existing social structures is effective, and might be valuable in a population with minimal access to education and little to no knowledge about contraceptives.