Remedial Courses Research Articles

Coloniality and Refugee Education in the United States

In this paper, we demonstrate the ways in which the schooling of refugee youth in the United States reflects ongoing coloniality in education. Drawing on data collected in a case study, conducted between 2013 and 2016, as part of a larger ongoing ethnography of a Southwest United States District school’s response to refugee students, we show how the enactment of policies, pedagogies, and practices within schools reinforce the government’s control over refugee students and their families. In schools, the students are kept out of certain school spaces, marginalized in remedial courses, and denied academic opportunities and integrated support services. Using empirical data, we demonstrate how the restriction of the students’ movement in and around schools is embedded within the larger limitations embedded in coloniality and assimilation. We situate our analysis within the tensions and interactions between coloniality, assimilation, and neoliberalism as articulated in studies within anthropology and sociology, migration studies, critical refugee studies, and cultural studies. We conclude with a call for the decolonization of education and offer a practical starting point in refugee education.

STEM Pathway and College Progression: The Link Between Engineering CTE and Postsecondary Outcomes for Students With Learning Disabilities

Despite growing calls to increase diversity in science, technology, engineering, and mathematics (STEM) fields, students with learning disabilities (SWLDs) remain underrepresented in STEM at the postsecondary level. Considering this call for increased diversity as a means to expand and strengthen STEM success, we used the High School Longitudinal Study of 2009 to explore how participation in engineering career and technical education (E-CTE) links to postsecondary educational outcomes for SWLDs. Particularly, we examined how E-CTE participation relates to postsecondary remedial course taking, enrollment in a 4-year postsecondary institution, and declaration of a STEM major. Results from school fixed-effects estimations suggest that each credit of E-CTE earned is associated with fewer remedial college courses, a higher likelihood of enrolling in a 4-year as opposed to sub-baccalaureate institution, and increased odds of declaring a STEM major. To conclude, we discuss the implications of our findings for both policymakers and practitioners.

Action Research in ESL: Enhancing English Oral Proficiency in an Asynchronous Speech Course

This action research investigated the impact of remedial collaborative techniques, pedagogical methods, and multimedia resources on enhancing oral language proficiency development among college English Language Learners (ELLs). Action research was used to gather data through a survey questionnaire and interviews with 25 ELLs enrolled in an asynchronous speech course. The participants’ experiences with the remedial course strategies were positive for practicing their English communication skills. The participants attributed their satisfaction to the ample opportunities for collaboration with peers and the instructor, the course structure, and relevant multimedia resources. Feedback emerged as a critical factor in learner engagement and progress. The results underscore the potential of asynchronous learning for fostering oral language proficiency. However, further research is required to validate these findings in different contexts. This study provides a foundation for a more comprehensive understanding of factors contributing to the holistic improvement of oral language proficiency in asynchronous settings for college ELLs.

Utilizing ALEKS and Standards Grading for Mathematics Placement and a Bridge Course for STEM Students

We present a system for math placement and intervention that pairs ALEKS PPL with an online, standards-based “bridge” course designed to prepare students interested in STEM for PreCalculus. To determine the efficacy of this system we studied ALEKS PPL placement scores, bridge course grades, ALEKS concept mastery percentages, and PreCalculus grades for a population of 175 students over a span of two years. The study was conducted at a small (∼800 undergraduates), private university with limited resources for placement processes and remedial course offerings. We find that around 95% of students placed into the bridge course passed the course and also significantly increased in mastery of ALEKS topics, with mean increases in all concept groups. We also find students who enroll in the bridge course receive similar PreCalculus grades compared to those who placed directly into PreCalculus.

Designing Remedial Course in English for Engineering Students from Non-English Medium Background

Engineering graduates in India belong to different fields of specialization. The prerequisites of the professions and the specificity of language functions required for a study of English offered at Engineering Colleges. The students who join professional courses like Engineering, half of the students come from Non-English medium Instruction. Careful analyses of these students’ performance by diagnosing the areas of difficulty will help to plan and design a remedial course to teach the basics to communication skills in order to enhance and present according to their contexts and their academics. The engineering colleges in Telangana and Andhra Pradesh have become mushrooms without maintaining any quality which should be suited to the industry requirement. The intention in preparing the paper is to point out what is lacking in engineering students and they are suited to the industry, their weaknesses, stumbling blocks, and measure to improve by suggesting with a remedial course in order to implement in engineering studies at the beginning of their courses.

Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable machine learning (IML) and explainable artificial intelligence (XAI) tools.In this work, using the data of a large public Hungarian university, we demonstrate how IML and XAI tools can support educational stakeholders in dropout prediction. We show that complex machine learning models – such as the CatBoost classifier – can efficiently identify at-risk students relying solely on pre-enrollment achievement measures, however, they lack interpretability. Applying IML tools, such as permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values, we demonstrate how the predictions can be explained both globally and locally. Explaining individual predictions opens up great opportunities for personalized intervention, for example by offering the right remedial courses or tutoring sessions. Finally, we present the results of a user study that evaluates whether higher education stakeholders find these tools interpretable and useful.

Home Economics Programme as a Tool for Marital Conflict Management

The study investigated Home Economics programme as a tool for marital conflict management. A descriptive survey was used for the study. The population consisted of all members of Home Economics Teachers’ Association of Nigeria (HETAN), Ondo State chapter. Sample of one hundred and sixty-three (163) respondents were drawn from the total population using simple random sampling technique. A self-developed structured and validated questionnaire was used for data collection. Data were analyzed using mean. Findings reveal among others lack of trust; parental interference; economic pressure and unrealistic expectation as part of the factors responsible for marital conflict. Findings also revealed mental health issue; inappropriate care for children; communication breakdown and economic instability as part of the implications of inappropriate handling of marital conflict in homes. Furthermore, decision-making process to attain family goals; principles of selection and utilization of family resources and homes and family in a changing world were revealed as part of the contents of Home Economics programme that could help in marital conflicts management. As people consider going for counselling in preparation for marriage, they should also consider taking at least a remedial course in Home Economics to further equip them for stable marital life.

Validating Reading Comprehension Assessment Under the GDINA Model

Cognitive diagnosis models (CDMs) are latent variable models mainly developed to assess students’ specific strengths and weaknesses in a set of skills or attributes within a particular domain. In this study, the reading comprehension assessment is diagnostically designed, constructed, and developed from the very first step. The predetermined attributes or sub-skills are explicitly defined in the construction phase as they should align with the instructional goals. Using R package CDM, the Generalized-DINA model (GDINA) was applied to the reading comprehension assessment. A total of 900 Year 4 primary students from the Eastern District of Pulau Pinang national and vernacular schools sat for this assessment. Through the cognitive analysis, the study is expected to provide detailed diagnostic feedback on students’ strengths and weaknesses in the underlying skills identified in the reading comprehension assessment. Such detailed information can help teachers in classroom teaching, designing remedial courses, and developing material according to the student's needs.

The Impact of Corequisite Math on Community College Student Outcomes: Evidence from Texas

Abstract Developmental education (dev-ed) aims to help students acquire knowledge and skills necessary to succeed in college-level coursework. The traditional prerequisite approach to postsecondary dev-ed—where students take remedial courses that do not count toward a credential—appears to stymie progress toward a degree. At community colleges across the country, most students require remediation in math, creating a barrier to college-level credits under the traditional approach. Corequisite coursework is a structural reform that places students directly into a college-level course in the same term they receive dev-ed support. Using administrative data from Texas community colleges and a regression discontinuity design, we examine whether corequisite math improves student success compared with traditional prerequisite dev-ed. We find that corequisite math quickly improves student completion of math requirements without any obvious drawbacks, but students in corequisite math were not substantially closer to degree completion than their peers in traditional dev-ed after three years.

Voluntary math remediation for STEM and economics disciplines – who is attending at all? Evidence from Germany

ABSTRACT Most studies on remedial courses are based on their mandatory attendance. However, changes may have occurred in the attendance policy of developmental math courses since the state of Florida decided to overcome obligatory math remediation for unprepared students. Consequently, researchers have recently started focusing on voluntary math remedial courses. In general, literature that goes back to the 1980s suggests that developmental coursework should be mandatory for unprepared first-year students. Since most universities in the US have always followed these recommendations, hardly any empirical evidence exists for the participation of students in voluntary remediation. Conversely, the remedial education system in Europe and particularly Germany is primarily voluntary. Therefore, this study exploratively examines the participation of students in two optional developmental math courses: a so-called preparatory course and a so-called bridging course. The findings suggest that summer-school-like preparatory courses miss their target group of at-risk students, whereas semester-running bridging courses reach it.

CONDUCT REMEDIAL COURSE IN DIFFERENT MODES

Remedial course on English Language is a special learning activity to provide additional learning opportunities for underachieving students to help them meet the minimal academic standards. The goal is to help under achievers to meet the minimum academic standards of the general curriculum. Farm Universities attracts over 40 per cent of students from rural background due to reservation for students from agriculture families. Though they are merit students, they lack proficiency in English language. Hence, remedial course are essential in farm Universities. It can be conducted as a regular class or flipped class mode. In addition, Language Lab can be established to facilitate learning of English Language. The remedial courses were conducted offline and flipped mode. The software in English Language lab consist eight major topics. It includes lecture videos, assignments and activities which are interactive. The pre and post evaluation was conducted for all programmes. The remedial course have created significant impact on the improved knowledge and speaking skills.

KHUTTAH `ILAJIYYAH `ALA ASÂS NAZARIYYAH TAHLÎL AL-AKHT‘ AL-LUGAWIYYAH FÎ TA`LÎM AL-LUGAH AL-`ARABIYYAH

The field of Arabic language education suffers from shortcomings in the adopted curriculum. Learners realize serious problems when learning Arabic in the classroom. The teacher is committed to solving their problems and makes a conscious effort to improve the educational content in the classroom. The theory of linguistic errors analysis plays an important role in identifying learners' learning problems and taking the necessary educational measures to overcome them. The study of linguistic errors makes a good contribution - theoretically - about the common linguistic characteristics in the teaching of the second language. In practice, the teacher assists in designing remedial courses by following the steps of analyzing language errors in extracting them. It complies with providing the appropriate material that prevents learners from repeating their linguistic error.

The long-run effects of college remedial education

We investigate the long-run impact of college remedial education on students’ academic performance and employment status. By implementing a Fuzzy Regression Discontinuity Design we show that attending remedial courses positively affects the probability to get a university degree, whereas no significant effect is found on labour market outcomes.

A Nonparametric Weighted Cognitive Diagnosis Model and Its Application on Remedial Instruction in a Small-Class Situation

CDMs can provide a discrete classification of mastery skills to diagnose relevant conceptions immediately for Education Sustainable Development. Due to the problem of parametric CDMs with only a few training sample sizes in small classroom teaching situations and the lack of a nonparametric model for classifying error patterns, two nonparametric weighted cognitive diagnosis models, NWSD and NWBD, for classifying mastery skills and knowledge bugs were proposed, respectively. In both, the variances of items with respect to the ideal responses were considered for computing the weighted Hamming distance, and the inverse distances between the observed and ideal responses were used as weights to obtain the probabilities of the mastering attributes of a student. Conversely, NWBD can classify students’ “bugs”, so teachers can provide suitable examples for precision assistance before teaching non-mastery skills. According to the experimental results on simulated and real datasets, the proposed methods outperform some standard methods in a small-class situation. The results also demonstrate that a remedial course with NWSD and NWBD is better than one with traditional group remedial teaching.

Co-requisite Remediation: An Access Ramp for College Completion

ABSTRACT Higher education’s interest in remedial college coursework and its bumpy impact on student success drove this collaboration between the mathematics department and institutional research at a small, private 4-year university. For first-time freshmen needing math remediation, national research shows that these students struggle with earning required college math credits compared with non-remedial students. A major roadblock for them is that they often do not earn credit towards their degree for remedial courses, further challenging progression through their major. This research describes the impact of one institution’s curricular innovation to prepare students more quickly for college-level math and have some of that credit count toward graduation, thus meeting the institution’s Liberal Arts Core requirement. The researchers examine a combined cohort of first-year students to determine if there is a relationship between this co-requisite math course and higher retention and graduation rates compared to other traditional types of mathematics remediation. Students taking the co-requisite remedial class retained and graduated at higher rates and were also more successful in subsequent college-level mathematics classes.

Integrating co-requisite math for STEM student success.

Gateway math courses can be a major hurdle for students to get over in order to continue on with their majors, especially STEM majors. Previously, students could have taken up to four semesters of remedial math before being able to place into the first gateway math course needed for their major. With Math Support, students no longer have to take those remedial courses; the remediation is built in to the course in addition to the regular level content. These co-requisite courses allow students to start into their major courses much sooner than in the past, therefore allowing them to stay on track in their major and finish successfully.
 Developmental math courses have been replaced with co-requisite and accelerated learning models. All students can now progress through gateway courses within their first year, regardless of test scores. Gateway courses are offered with additional support to aid students in learning and understanding and fill in any gaps. Fairmont State was one the first colleges in WV to offer these services at full-scale. Attendees will be given an overview of Math Support at Fairmont State and how we have adapted our course to meet the changing needs of students as a result of the COVID pandemic. More data will be reported.

Deskripsi Belajar Anatomi Tingkat Kesulitan pada Sistem Online dan Offline Mahasiswa Fakultas Kedokteran, Wijaya Kusuma Surabaya

Anatomy is a basic science that is learned in medical education as a basis related to clinical skills in the skills to become a doctor. The Covid-19 outbreak has changed the entire education system. The Covid-19 pandemic has changed the shape of the education model at universities until the Faculty of Medicine is closed. The COVID-19 pandemic has changed the pattern of the learning system, from which the offline model was changed to online. This learning pattern forces students to usually participate in online activities. Bring up the pattern of levels that appear in the two models of this learning system. The cross sectional method and observational research were then analyzed and the descriptions were observed. simple random sampling method was used in this study. The purpose of this research is to understand descriptive anatomy learning, seen from the level that appears in students' difficulties, obtained from 20 questionnaires consisting of 7 categories, namely: understanding, effectiveness, good communication, learning hours, clear material, easily accessible lectures. There were no students who took part in the remedial course (RC). From the results of filling out the questionnaire and processing the data, it can be concluded that there is no picture of the difficulties between the two learning systems, namely online and face-to-face.

Identifying Critical Factors When Predicting Remedial Mathematics Completion Rates

Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study is to build machine learning models that can predict students that are least likely to complete remedial mathematics and identify which factors are most influential when computing those predictions. The study discovers random forest as the highest performing model. Furthermore, the study reveals that the remedial course a student begins with, credit completion rate, math placement score, and high school G.P.A as the most influential predictors of completion rates. The study also offers future research directions, especially in how to improve the performance of the machine learning models.

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images. Moreover, deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis. Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging. This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning. It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.

The role of geophysics in the construction and stability assessment of tunnels

Geophysics play a vital role in the constructions of any major manmade structures in the world. One of those being the tunnels. In depth understanding of geophysical methods and a lot of information are needed in order to design a tunnel construction project. Comprehensive investigation on the ground condition has to be done before the field preparation study that will determine the stand-up time and the groundwater condition that may disrupt the tunnel construction. For tunnel stability assessment, an integration of geophysical methods is a must in order to obtain the most accurate results. Satellite imaging interpretation emphasizes on the structural tracing of negative lineament while field mapping emphasizes on location of underground seepage and major tectonic structures such as faults, joints and shear zones. Geoelectrical resistivity tomography survey is able to identify the differences in resistivity of Earth’s materials based on the water content inside of them. The best course of remediation could only be chosen once the output from all these studies are made available.