The Relationship Between Academic-Efficacy and Persistence in Adult Remedial Education: A Replication Study

Self-efficacy is considered a construct influencing persistence (Bandura 1997, 2001, 2012). For adults pursuing academic remediation in preparation for higher education, persistence is a specific barrier to success in approximately 50% of cases. This study examined the relationships between general self-efficacy and academic-efficacy constructs with adult remedial education persistence for N = 88 students, and found a lack of relationship consistent with the earlier sample of students (Holmquist, Gable, & Billups, 2013). Further, few relationships were found with selected student demographic characteristics

Admissions Counselors’ Perceptions of Cognitive, Affective, and Behavioral Correlates of Student Success at an Independent High School: A Mixed Methods Study

“Through the recruitment, selection, and enrollment of students, admission and enrollment management professionals play a critical role in their schools’ vitality and educational culture” (NAIS, 2012, para. 2). According to the Principles of Good Practice, stated by NAIS (2012), through the admission process schools seek to ensure an appropriate match between prospective students/families and the school. For admission professionals to make the most effective decisions for both the school and applicant, they gather materials to get to know the student on a deeper level. These materials include, but are not limited to, a formal application, transcripts (often from the past 2 ½ years), two or more teacher recommendations from current teachers, a school visit, on-campus interview, and admission test scores. There is limited evidence to demonstrate the attributes that admission counselors find important to academic success beyond test scores and quantitative evidence gathered during the admission process. There is an abundance of evidence supporting cognitive, affective and behavioral attributes, which lend themselves to success in 21st century learners (Bandura, 1977, 1986, 1997; Costa & Kallick, 2000; Gardner, 1999; Hayes-Jacobs, 2010; Sternberg, 1999, 2010), but limited evidence of how admission counselors are measuring these attributes. The purpose of this research was to identify attributes within the cognitive, affective, and behavioral domains that Admission Counselors feel are essential to student success in school and life

Patterns of District Performance in Student Achievement: Connecting Resources to Student Achievement

This presentation is the first sequence of a three-phase study using a mixed method sequential explanatory strategy (Creswell, 2003). The study is research in-progress that investigates how resources can increase or diminish the value resources as they move through the education delivery system contributing in variations in its overall performance (Porter, 1985). The study is unique, because it combines, and is based on microeconomic and complex adaptive theories to examine resource utilization within school districts. This first sequence has two analytical goals and steps: (1) to verify the significant correlation, but with patterns of variability for district performance measured by student achievement as the dependent variable and Socioeconomic Status (SES) indicators as the independent variable Gaudet, 2000; Walberg, 2006); and (2) to identify distinct patterns of district performance over multiple years that include sustained over-performance, stagnation, decline and possible turnarounds. This is a simple regression analysis that utilizes SES as a predictor variable for district performance. The patterns of district performance are measured by comparing a statistically-predicted performance value with actual performance. The variability of performance over multiple years will inform the second sequence that examines the nature and strength of patterns of resource decision-making and utilization compared outcomes among school districts along the spectrum of socioeconomics, demographics and scale. Gaudet’s (2000) explanation for the variance between actual and SES-predicted student achievement for outperforming districts supports the central tenet, which is that, “some school districts add value to the learning readiness of their students” (p.3)

Connecting Resources to Student Achievement: Assessment of the Indeterminacy of District Performance

The purpose of this study is to conduct cluster analyses, resulting in groupings of N=113 districts based on socioeconomic status (SES), which is the independent variable and primary correlate of performance. It is a quantitative analysis of N=113 districts in Massachusetts for the period from 2000 to 2005. The study conducts cluster analyses to evaluate district performance as measured by student achievement. The problem is stated by National Research Council (1999) that: “Indeterminacy characterizes education production”. Indeterminacy is represented by variation in the N=113 districts’ performance. The groupings of performance obtained from the cluster analyses provide information about the types and magnitude of indeterminacy. The methodology is based on inductive pattern recognition (Trochim (1985). Hierarchical Cluster Analysis (HCA) is used to group districts along a performance continuum and assess variability between SES and district performance. The hypothesis of the study is that variation in performance relates to change in capacity which derives from positive or negative transformation of resources as they are processed by organizations (Porter, 1985

An Investigation of a Methodology to Assess District Performance

This study investigates a resource-based methodology to assess district performance as an indicator of student achievement on standardized assessments. The problem that this investigation addresses is that performance measurement and the associated decision-making is indeterminate. There is a lack of empirical research that relates decision-making about resource utilization to performance. The study utilizes structuralism to assess the relationship between the independent variable of resource utilization and the dependent variable performance. Complex Adaptive System theory is used as a framework for Concept Mapping methodology. The study is grounded in theories from Complex Adaptive Systems and Microeconomics that state that performance is a function of capacity. An adaptation of the generic value chain (Porter, 1985) is designed as a representation of the education delivery systems for N=7 districts. Previous sequences in this research project have established performance levels and variations from the independent variable of socioeconomic status (Simpson, Kite, & Gable, 2007). The concept maps illustrate the nature, magnitude, strength and underlying relationships for thematic patterns of resource utilization for the N=7 districts. The concept maps provide an explanation for some of the variation in performance that does not relate to socioeconomic status. The explanation of variability in performance represented by the concept maps is intended for diagnostic applications, not to establish best-practices that can be transferred from high performing to low performing districts. The primary application of the methodology is for strategic or intervention planning

Assessment of students\u27 knowledge of Internet risk and Internet behaviors: Potential threats to bullying and contact by Internet predators

Given the serious issue of bullying, this study sought to assess middle and high school students\u27 knowledge of appropriate use and their behaviors on the Internet and social networking sites, especially regarding behaviors that may lead to cyberbullying or contact with potential Internet predators. Three school districts (urban, suburban, and urban ring) with grades 6 - 12 are participating in this study. Differences among and between grade levels, gender, and school demographics at the dimension and item-level will be presented

Parental Involvement in Students’ Safe Use of the Internet

The purpose of this study was to investigate if parental involvement in digital activities relates to middle school students’ knowledge of appropriate use of the Internet and social networking sites. Parental involvement, measured using a three-item dimension on the 40 item instrument, asked students to report on their knowledge of their parent’s involvement with their internet activity. The aggregate score on this dimension was used to measure the relationship among several dimensions. Furthermore, demographic items, such as grade level, having an older sibling, and getting in trouble at school, were also investigated. Over 71% of adults in the United States use the Internet (Horigan, 2007). Research suggests that adolescence (namely teens), are heavier users than adults (Subrahmanyam, Kraut, Greenfield, & Gross, 2001). Actually, in the United States, it is estimated that 21 million teens use the Internet. This represents 87% of this age group (Lenhart, 2005). Student have access to the Internet readily available, be it school, home, or library. This ease of access may increase the potential for students to become victims of Internet sexual predators or other students who engage in inappropriate cyberbullying behaviors. Rainie (2008) found that 32% of teens reported being contacted on-line by a stranger. Furthermore, 23% (of the 32%) stated that the contact made them feel scared or uncomfortable. There is a myriad of evidence to support the need for parental involvement in a child’s internet activities, from filtering access to monitoring activity, supervision is paramount (Lenhardt, 2005; Raine, 2008; Shariff 2008). Aside from the fact that predators are seeking young predators, teens are also reporting inappropriate behaviors. In fact, Lenhardt found that 81% of parents and 79% of teens agreed that “teens are not careful enough when sharing personal information on-line” (pii). Furthermore, when asked if “teens do things online that they wouldn’t want their parents to know about” (pii), 65% of the parents and 64% of the teens agreed with the statement. The knowledge of the issue is evident from both parties, so now what do we do with it? This line of research aims to understand the status of behaviors and views of middle school students and the influence parents have on these behaviors. It is hoped that the results may assist schools in developing educational programs and safeguards to protect students

Anxiety and Depression as Comorbid Factors in Drinking Behaviors of Undergraduate College Students Attending an Urban Private University in the Northeastern United States

High-risk drinking is the number one public health concern on college campuses (Berkowitz, 2003; Kapner, 2003; Wechsler, 2002). To date, high-risk drinking prevention programs have met with limited success (Kapner, 2003). This study examined differences among four drinking behavior groups: non-drinkers [(ND), (n = 128)], low-risk drinkers [(LRD), (n = 252)], high-risk drinkers [(HRD), (n = 272)], and frequent high-risk drinkers [(FHRD), (n = 290)] with respect to anxiety and depression for male (n = 457) and female (n = 485) undergraduates (N = 942) attending an urban private university in the northeastern United States; and, the perceptions of two undergraduate focus groups (N = 10) and one faculty/staff group (N = 14) for why undergraduates engage in high-risk drinking and actions to reduce this behavior. Volunteer participants completed a demographic questionnaire, the Alcohol Use Disorders Identification Test, the Beck Anxiety Inventory, and the Beck Depression Inventory. An ANOVA indicated differences among the groups with respect to anxiety (F = 6.49, p \u3c .001), but not with respect to depression. The FHRD group had higher anxiety (M = .68) than the ND group (M = .33) and the LRD group (M = .44). A t-test indicated differences (p \u3c .01) in the level of anxiety between HRD females (M = .69) and HRD males (M = .40), with no differences for depression. A chi-square analysis indicated differences between males and females with respect to drinking behavior group classification (χ² = 22.40, df = 3, p = .001). Focus group results suggested several reasons why students engage in high-risk drinking: it is the norm, easy access to alcohol, low accountability for drinking, cope with anxiety, relieve boredom, lift depression, cope with anger, family history of alcohol use, alcohol dependence, and poor self-esteem. Implications for educators are discussed