Rural “at-risk” students – Directions for policy and intervention

This article includes excerpts from case studies of at-risk students. It discusses the high prevalence of at-risk students in rural areas, the relationship of this phenomena to rural cultures, and basic conditions associated with being “at-risk.” Necessary policy and social changes are described and how these can be achieved within the context of a given rural community. School and community preventive and treatment services are detailed. The article discusses the need to address the “secondary” disability (the emotional overlay) of an at-risk handicapped student to facilitate effective intervention with the “primary” disability. The article concludes with a description of the need for systemic change including an emphasis on self-esteem education, appropriate pre-service and inservice, community-business-school partnerships, family involvement, and community education.

Download Full-text

Implementation of a study skills program for entering at-risk medical students

AJP Advances in Physiology Education ◽

10.1152/advan.00022.2014 ◽

2014 ◽

Vol 38 (3) ◽

pp. 229-234 ◽

Cited By ~ 11

Author(s):

Cynthia J. Miller

Keyword(s):

At Risk ◽

Medical Students ◽

Medical School ◽

Rural Areas ◽

Study Skills ◽

At Risk Students ◽

Age Groups ◽

First Year ◽

The Past ◽

Didactic Lecture

While the first year of medical school is challenging for all students, there may be specific issues for students from rural areas, economically disadvantaged backgrounds, ethnic minorities, or nontraditional age groups. A Summer Prematriculation Program (SPP) was created to prepare entering at-risk students for the demands of medical school. For the past 2 yr, an emphasis was placed on the development of appropriate study plans and skills. On presurveys, students predicted an increase in their number of study hours per lecture hour, from 7.6 h in undergraduate coursework to 9.1 h in medical school coursework ( n = 35). These study plans were infeasible given the rigorous didactic lecture schedule in medical school. Interventions were made through lectures on study plans and modeling of appropriate study habits using engaging lectures in the SPP physiology course. At the end of the program, a postsurvey was given, and students reported a reduction in the planned hours of study to a more realistic 3.9 h of study time per hour of lecture. Furthermore, students planned to decrease their use of textbooks while increasing their use of concept mapping, videos, and peer teaching. The majority of students completing the SPP program with a study skills emphasis performed well in the Medical Physiology course, with 4 students honoring in the course, 27 students passing, and 2 students remediating the course after an initial failure. These results indicate that at-risk medical students may have inappropriate study plans that can be improved through participation in a program that emphasizes study skills development.

Download Full-text

Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

Information ◽

10.3390/info11100474 ◽

2020 ◽

Vol 11 (10) ◽

pp. 474 ◽

Cited By ~ 1

Author(s):

Yanbai He ◽

Rui Chen ◽

Xinya Li ◽

Chuanyan Hao ◽

Sijiang Liu ◽

...

Keyword(s):

Neural Network ◽

At Risk ◽

At Risk Students ◽

Modern Society ◽

Virtual Learning Environment ◽

Peak Performance ◽

Sequential Data ◽

Stream Data ◽

Learning Platforms ◽

Relationship Of

Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.

Download Full-text

Phonological Awareness Screening to Identify At-Risk Readers

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.2902.109 ◽

1998 ◽

Vol 29 (2) ◽

pp. 109-116 ◽

Cited By ~ 14

Author(s):

Margie Gilbertson ◽

Ronald K. Bramlett

Keyword(s):

At Risk ◽

Phonological Awareness ◽

Reading Ability ◽

At Risk Students ◽

First Grade ◽

Receptive Vocabulary ◽

Standardized Assessments ◽

Cutoff Score ◽

Invented Spelling ◽

Reading Measures

The purpose of this study was to investigate informal phonological awareness measures as predictors of first-grade broad reading ability. Subjects were 91 former Head Start students who were administered standardized assessments of cognitive ability and receptive vocabulary, and informal phonological awareness measures during kindergarten and early first grade. Regression analyses indicated that three phonological awareness tasks, Invented Spelling, Categorization, and Blending, were the most predictive of standardized reading measures obtained at the end of first grade. Discriminant analyses indicated that these three phonological awareness tasks correctly identified at-risk students with 92% accuracy. Clinical use of a cutoff score for these measures is suggested, along with general intervention guidelines for practicing clinicians.

Download Full-text