Reading Instruction for Students with Emotional Disturbance: A Mixed-Methods Investigation.

Although there is a substantial body of observation research investigating the manner in which reading instruction is provided to students with learning disabilities, there is little research in this area involving students with and at risk for emotional disturbance. The purpose of this investigation was to contribute to the limited corpus of observation studies investigating school-based practice in reading for this student population. In this investigation, 11 teachers from two states were systematically observed while providing reading instruction over the course of the 2017-2018 school year. Participating students were also observed over the course of the year and completed two standardized reading assessments at the beginning and end of this investigation. Teachers were also interviewed to identify contextual factors that promote or impede the provision of high quality reading instruction to this student population. Study findings suggest that teachers are in need of additional training, support, and resources to maximize instructional time. Students in this sample tended to make no or minimal progress in reading and were frequently observed displaying low levels of academic engagement across settings. Implications for school practice and areas for future research are discussed

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Motivation as a predictor of dental students’ affective and behavioral outcomes: Does the quality of motivation matter?

Since the motivation to study and engage in academic activities plays a key role in students’ learning experience and well-being, gaining a better understanding of dental students’ motivations can help educators implement interventions to support students’ optimal motivations. The aim of this study, grounded in self-determination theory, was to determine the predictive role of different types of motivation (autonomous motivation, controlled motivation, and amotivation) in the affective and behavioral outcomes of dental students. Amotivation is the absence of drive to pursue an activity due to a failure to establish relationships between activity and behavior; controlled motivation involves behaving under external pressure or demands; and autonomous motivation is an internalized behavior with a full sense of volition, interest, choice, and self-determination. A cross-sectional correlational study was conducted in 2016, in which 924 students (90.2% response rate) from years one to six agreed to participate, granting permission to access their current GPAs and completing four self-reported questionnaires on academic motivation, study strategies, vitality, and self-esteem. The results showed that self-determined motivation (i.e., autonomous over controlled motivation) was positively associated with vitality, self-esteem, and deep study strategies and negatively associated with surface study strategies. The contrary results were found for amotivation. In the motivational model, deep study strategies showed a positive association with students’ academic performance. Contrary results were found for surface study strategies. This study extends understanding of the differentiation of motivation based on its quality types and suggests that being motivated does not necessarily lead to positive educational outcomes. Autonomous motivation, in contrast to controlled motivation and amotivation, should be supported to benefit students with regard to their approaches to learning and well-being since it can promote students’ vitality, self-esteem, deep over surface study strategies, and enhanced academic performance

Engaging Students Through Collaboration: How Project FUN Works

Students from three disciplines designed, developed, and implemented exercise and nutrition interventions, online modules and videos, to benefit low-income middle school students. The process used to incorporate the scholarship of teaching into a collaborative college-level application of learning is described

What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Serving High-Risk Youth in Context: Perspectives from Hong Kong

Background: High-risk youth are often defined in occupational therapy terminology as adolescents and young adults who experience personal, contextual, or environmental barriers to effective participation in healthy, age-appropriate occupations. Without assistance for participation, these youth may acquiesce to daily routines of unhealthy risk-taking or isolation, failing to achieve developmental milestones needed for successful transition to adulthood. There are known therapeutic services targeting this population, but occupational therapy involvements have been sparsely documented. Method: Having been affiliated with a community-based occupational therapy program serving high-risk youth for many years in the US, the principal investigator of the study used a sabbatical opportunity to explore services provided to high-risk youth in Hong Kong (HK). This paper reports preliminary findings obtained from an exploratory study of analyzing transcripts of 13 one-on-one interviews with service providers in HK. Results: Two major themes are discussed in this paper: the prevalent behavioral risks among high-risk youth as perceived by the service providers and the intervention approaches used by the service providers with the high-risk youth population in HK. Conclusion: Reflecting on the preliminary outcome of the study, the authors suggest that occupational therapy may contribute to mitigating youths’ risk factors through ecological occupational engagement

Toward a Systematic Evidence-Base for Science in Out-of-School Time: The Role of Assessment

Analyzes the tools used in assessments of afterschool and summer science programs, explores the need for comprehensive tools for comparisons across programs, and discusses the most effective structure and format for such a tool. Includes recommendations

Individual Differences in Cyber Security

A survey of IT professionals suggested that despite technological advancement and organizational procedures to prevent cyber-attacks, users are still the weakest link in cyber security (Crossler, 2013). This suggests it is important to discover what individual differences may cause a user to be more or less vulnerable to cyber security threats. Cyber security knowledge has been shown to lead to increased learning and proactive cyber security behavior (CSB). Self-efficacy has been shown to be a strong predictor of a user’s intended behavior. Traits such as neuroticism have been shown to negatively influence cyber security knowledge and self-efficacy, which may hinder CSB. In discovering what individual traits may predict CSB, users and designers may be able to implement solutions to improve CSB. In this study, 183 undergraduate students at San José State University completed an online survey. Students completed surveys of self-efficacy in information security, and cyber security behavioral intention, as well as a personality inventory and a semantic cyber security knowledge quiz. Correlational analyses were conducted to test hypotheses related to individual traits expected to predict CSB. Results included a negative relationship between neuroticism and self-efficacy and a positive relationship between self-efficacy and CSB. Overall, the results support the conclusion that individual differences can predict self-efficacy and intention to engage in CSB. Future research is needed to investigate whether CSB is influenced by traits such as neuroticism, if CSB can be improved through video games, and which are the causal directions of these effects