At-risk Students Research Articles

Overcoming the Research–Practice Gap: Key Considerations for Speech-Language Pathologists Related to Multitiered Systems of Support

Purpose: This tutorial highlights the challenges and opportunities school-based speech-language pathologists (SLPs) face related to engagement with the multitiered systems of support (MTSS) framework in their public school work practices. It positions SLP involvement in MTSS as an area where a gap between research and practice exists and highlights key ways for SLPs to overcome barriers to support student success within this framework. Method: This tutorial provides a general description of the MTSS framework and highlights the immediate applicability of the framework to SLP school practices. Key considerations are offered for SLPs ready to take concrete steps related to implementing the MTSS framework. Hypothetical cases are used to illustrate how school-based SLPs can use these considerations to guide meaningful but realistic changes. Results: Ongoing concerns about the impact of the pandemic on student outcomes and growing awareness of systemic inequities in education are prompting SLPs to move beyond asking if they should be involved in MTSS to focusing on how they should be engaged with this framework. This tutorial provides five key considerations for SLPs to realistically deepen their engagement with MTSS and overcome the research–practice gap in this area. Conclusion: This tutorial provides a resource for SLPs to take concrete and immediate steps to support at-risk students through MTSS given the needs at this unique moment in time when concerns about learning loss and achievement gaps are front and center in public schools.

Clinical Assessment Specialists: A New Faculty Role to Improve Clerkship Assessment.

Ambulatory clerkships, including longitudinal integrated clerkships (LICs), face challenges to assessment, including time pressure and clinical demands on preceptors. High-quality clinical assessment is critical to implementing competency-based medical education, generating valid grades, and supporting learning. This importance is further heightened with the new pass/fail scoring for US Medical Licensing Exam Step 1, discontinuation of US Medical Licensing Exam Step 2 Clinical Skills, and the growing concern for bias in assessment. The Kaiser Permanente Bernard J. Tyson School of Medicine's LIC spans the first 2 years with 50 students per class. In 2021-2022, the authors created a new faculty role, the clinical assessment specialist (CASp). CASps are highly trained clinical teachers who directly observe clerkship students in the ambulatory setting, provide feedback, and complete competency-based assessment forms. CASps completed 186 assessments of first-year (Y1) LIC students and 333 assessments of second-year (Y2) LIC students. Y2 students achieved average higher milestones and were rated as requiring less supervision compared to Y1 students. Y1 students rated CASps more favorably than Y2 students. Preceptors rated the contribution of CASps similarly across both years. Clerkship directors described benefits including identification of at-risk students and value of augmenting preceptor assessments. The CASp role may offer an innovative way to generate valid assessment of student performance, offset clinical pressures faced by preceptors, identify at-risk students, and mitigate bias, especially in an LIC. Future studies may examine assessment validity, including use in summative assessment. CASps are an innovative approach to clinical clerkship assessment.

Relationship Between Anxiety, Coping Strategies, and Perfectionism in Entry-level Doctor of Physical Therapy Students

Introduction. Doctor of Physical Therapy (DPT) students must meet high standards throughout their lives and careers, which can cause stress and anxiety, with potentially devastating impacts. This quantitative study examined whether and to what extent perfectionism, demographic characteristics, and coping strategies relate to state (situation-related) and trait (personality-related) anxiety in DPT students. Review of Literature. Existing literature demonstrates high anxiety levels in DPT students; also, maladaptive perfectionism is associated with higher levels of stress. The predictive relationship between coping, perfectionism, and anxiety in DPT students has not been assessed. Subjects. Convenience sample of DPT students in their first through third years across 8 different DPT programs (n = 206). Methods. This paper was a cross-sectional, nonexperimental, multisite project. A hierarchical linear regression assessed the relationship between variables (demographics, coping, perfectionism, state anxiety, and trait anxiety). Results. Approximately 81.5% of the sample reported being not a perfectionist, 7.0% maladaptive perfectionists, and 11.5% adaptive perfectionists. Students using adaptive coping comprised 72.3% of the sample, whereas 28.0% used primarily maladaptive coping mechanisms. State Trait Anxiety Inventory scores were higher for this sample of DPT students when compared with published studies in the normal population. Significant predictor variables for state anxiety were sex, type of coping strategies used, and class year in the DPT program. For trait anxiety, sex, coping strategy type, maladaptive perfectionism, and class year were significant. Discussion and Conclusion. There is a predictive relationship between sex, type of coping strategies used, and year within the DPT program for both state and trait anxiety in DPT students. Presence of maladaptive perfectionism was an additional predictive variable for trait anxiety. This knowledge can help educational institutions better identify at-risk students for anxiety and tailor programs to better meet the individual needs of our students.

책임지도교수가 인식하는 학사지도 상담에서의 어려움

This study was conducted for the purpose of exploring the difficulties experienced in academic advising by professors participating in the responsible advisor system, a Faculty-Only Model. For this purpose, data was collected from nine professors working at K University and analyzed using concept-mapping research methods. The results are as follows. First, the perception of difficulties in academic advising was classified into two dimensions: ‘student characteristics’ - ‘academic advising system’, ‘non professors factors’ - ‘professors factors’. Second, there were six types of clusters of difficulties, and the order of importance of the types is as follows : “response to students who need linkage with counseling center”, “difficulties in providing appropriate intervention for students with various characteristics, including at-risk students”, “lack of systematic training and guidelines”, “students’ attitudes and readiness toward academic advising”, “helplessness felt by professors in areas where advice is needed.”, and “difficulties caused by the academic advice system and physical environment”. Based on the results, implications for the responsible advisor system were proposed.

Innovative Strategies for Fostering Student Engagement and Collaborative Learning among Extended Curriculum Programme Students

Massification in higher education has made the satisfaction of students’ needs unattainable among university lecturers. In contrast, the use of innovative strategies for collaborative learning enhances student engagement in the heterogeneous student context. Moreover, this paper aims to explore strategies for fostering student engagement through collaborative learning among first-year students at the University of Venda. To answer the research question of whether innovative learning designs in teaching and learning can foster students’ engagement and collaborative learning, subsequently, this paper employed a mixed-methods approach to gauge the effectiveness of different teaching strategies on student outcomes. Significantly, the purposive sampling approach was used to sample a group of 200 students in the Extended Curriculum Programme (ECP). The researchers used participant observation and narrative inquiry as data collection instruments in this paper. Furthermore, the students in their respective groups were assigned topics from the English and Biology modules to use their talents to demonstrate their understanding of certain complex concepts in these modules. The non-surprising findings of this paper elucidated that collaborative learning expedites students’ mastery of key concepts and subject content through the integration of talents. The module lecturers introduced students to these innovative designs to ensure collaborative learning and effective student engagement. The key findings articulated that these aspects, namely role-playing exercises, group projects, peer-to-peer learning, use of talents, peer feedback, and so forth, have the potential to improve student performance and academic success. The implication of this study is that students learn to work together, delegate responsibilities, improve participation, and communicate effectively to attain a common goal. Using these strategies, lecturers promote collaboration among students and foster a more engaging and interactive learning experience. This paper further recommends the integration of students’ talents as an effective strategy to foster student engagement and collaborative learning to track and monitor at-risk students at an early stage.

Effectiveness of tutoring at school: A machine learning evaluation

Tutoring programs are effective in reducing school failures among at-risk students. However, there is still room for improvement in maximising the social returns they provide on investments.Many factors and components can affect student engagement in a program and academic success. This complexity presents a challenge for Public Administrations to use their budgets as efficiently as possible. Our research focuses on providing public administration with advanced decision-making tools.First, we analyse a database with information on 2066 students of the Programa para la Mejora de Éxito Educativo (Programme for the Improvement of Academic Success) of the Junta de Comunidades de Castilla y Léon in Spain, in 2018–2019, the academic year previous to the pandemic. This program is designed to help schools with students at risk of failure in Spanish, literature, mathematics, and English. We developed a machine learning model (ML) based on Kohonen self-organising maps (SOMs), which are a type of unsupervised (ANN), to group students based on their characteristics, the type of tutoring program in which they were enrolled, and their results in both the completion of the program and the 4th year of Compulsory Secondary Education (ESO).Second, we evaluated the results of tutoring programs and identified and explained how different factors and components affect student engagement and academic success.Our findings provide Public Administrations with better decision-making tools to evaluate and measure the results of tutoring programs in terms of social return on investment, improve the design of these programs, and choose the students to enrol.

Latent Profile Analysis of Urges for Suicidal and Self-Harming Behavior: The Role of Social Support Instability

College counseling centers are seeing increasing rates of suicidal thoughts and behaviors, and nonsuicidal self-injury (NSSI) among students. The high degree of need and limited resources underscores the need for novel approaches to identify at-risk students. We used latent profile analysis (LPA) to identify subgroups of students (n = 371) with different patterns of interpersonal factors and examine whether these subgroups differed by risk for self-injurious thoughts and behaviors. The best-fitting LPA had three profiles, which differed in urges to die by suicide and engage in NSSI. The profile with low average social support and higher instability (greater day-to-day fluctuations of self-reported social support and rejection) was associated with greatest risk, suggesting that this combination leaves individuals more vulnerable to suicide and NSSI.

Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

Background One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and support. Method The specific objective of this study was to build a model that identifies at-risk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or failure. Results The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.

H. Lefebvre의 공간생산론을 적용한 상담공간

Objectives In this study, we will apply H. Lefebvre's spatial theory (2014) to the Wee class.
 Methods To this end, we conducted a literature study of the works of Lefèbvre, who emphasized the dialectical dynamics of space, and the works of various scholars on consulting spaces.
 Results H. Lefebvre's theory of space is that a person creates a space, occupies and lives in the space, and then the space affects people's conception. By applying this, the counseling related person creates a space called Wee class, the counseling related person occupies the Wee class, and the Wee class occupant is influenced by the space again, suggesting a process of dialectical change in personal concept. Specifically, it is to clarify the mean-ing of Lefebvre's theory of spatial production and spatial tripletness, and to obtain concrete examples and results by actually applying them to the Wee class of A school. To this end, this study first discusses Lefebvre's philoso-phy and spatial theory, and then explores the meaning of Wee class space starting from a general educational space. For the study, the person in charge of the school counseling space (1 person), superintendent (1 person), principal (1 person), teachers (3 people), and students (5 people) participated in the study and conducted research on their experiences. For these students, the humiliation experienced in the Wee class space was divided into ‘spatial practice’, ‘representation of space’, and ‘space of representation’. Through this, if Lefebvre's spatial triplet is applied to Wee class space, ‘spatial practice' is related to the physical production of Wee class space, and ‘representation of space’ is related to expectations of Wee class space, providing a safety net for at-risk students, etc. Providing a space for emotional rest for students. Lastly, the ‘space of representation’ refers to the diverse and open experiences of students who are actual users of the Wee class space.
 Conclusions Applied to the Wee class through Lefebvre's theory of production and spatial tripartity, ‘spatial prac-tice’ is the planning of the installation of the Wee class and the actual occupation of the space of the Wee class. In addition, ‘reproduction of space’ is a common belief about the WEE class, and ‘space of representation’ is the various reactions of actual users to the WEE class. In this way, a variety of approaches to the WEE class space over time and among members will help to improve the quality of WEE class activities.

Openness to World Music Predicts Adaptation to a New Culture among Student Sojourners

Student sojourners temporarily live in a foreign country to pursue higher education. Research has shown that student sojourners’ openness to new experience and cultural values predict their adaptation to the host culture. However, it is unclear whether music – a major medium of cultural communication – also plays a role in adaptation to a host culture. This study examined whether student sojourners’ world music open-earedness (a willingness to explore, listen to, tolerate, and learn about music from diverse cultures) and functions of music in intercultural settings (the psychosocial reasons people engage with music) predict psychological and sociocultural adaptation. Seventy-six student sojourners in Australia reported their musical inclinations and adaptation to Australian society. World music open-earedness was significantly correlated with psychological and sociocultural adaptation. However, multiple regression modelling revealed that after statistically controlling for personal characteristics (e.g., age, musical training), personality traits (e.g., openness), and acculturation strategies, psychological adaptation was predicted by the music functions ‘arousal and activation’ and ‘self-reflection’, whereas sociocultural adaptation was predicted by world music open-earedness and the music function ‘arousal and activation’. Mechanisms that account for these associations and implications for identifying at-risk international students are discussed.

The Role of Relationships in Resilience: Teachers Who Were At-Risk Youth Supporting At-Risk Students

Despite extensive research on resilience, little information exists on the resilience of teachers who faced adversity in childhood and the meanings they attribute to their current work with at-risk students. Thus, this study aimed to address this knowledge gap by examining the narratives of teachers who were at-risk children. Based on 30 semi-structured life-narrative interviews, the findings revealed the significant meaning that the participants ascribed to positive relationships. Whether such relationships were present or absent in their childhood, these relationships were instrumental in their choice to pursue a career in education. In supporting their students’ resilience and becoming the teachers they wanted as children, they found meaning in their past experiences of risk and coping. The study’s discussion applies the social mirroring theory to analyze how the participants resisted the negative image presented to them by others in their childhood and how a belief of hopeful prospects fortified them and led them to take on transformative justice as their life mission. Supporting at-risk children functioned as a form of healing for the participants, fostering both their students’ and their own resilience. The study findings highlight the importance of accounting for teachers’ childhood experiences as formative narratives that mold their educational work.

Predicting Academic Student Performance based on e-Learning Platform Engagement using Learning Management System Data

The identification of at-risk students has become increasingly more significant as these students are in the precarious position of failing their courses. This study aims to achieve the objective of proposing a student performance prediction model to identify the stage of the course where at-risk students (students with the highest potential of failing their courses) can be identified based on student information system and learning management system data. The proposed student performance prediction model leverages machine learning methods to predict at-risk students, combining data from Universiti Putra Malaysia’s (UPM) Student Information System (SIS) and learning management system (PutraBlast). Two experiments were conducted to satisfy the objective. The first experiment uses the full semester data to test multiple machine learning models to identify the best model for this dataset. In the second experiment, the dataset was separated into four course stages with four predictive models trained on each stage. Students. Results show that GB outperforms other classifiers when trained on the full semester data. However, classifier performance decreases when trained on data from earlier stages of the course. Hence, based on these results, the earliest stage to predict at-risk students is identified to be the W1—W12 stage.

Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement. Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education.

Effectiveness of a localised and systematically developed gatekeeper training program in preventing suicide among Chinese adolescents.

To combat the pressing issue of contemporary suicide rates, an effective Life Gatekeeper training program was developed to educate school teachers in identifying and intervening with at-risk students. Two single-arm sequential studies evaluated the program's effectiveness, spanning implementation science stages from design to refinement.The initial study employed face-to-face training (FTF), followed by a standardized video-based 'Train-the-trainer' (TTT) approach. In Study 1, post-intervention and one-month follow-up results showed improved suicide literacy, reduced stigma, and increased willingness to intervene among gatekeepers. The revised TTT program (study 2) also yielded reduced stigmatization and improved intervention competence. In addition, six out of twenty teachers exhibited gatekeeper behaviors. In conclusion, both delivery methods proved effective, particularly the practical application of the TTT version, although further research is warranted to examine long-term effectiveness of the program.

Predicting dropout from higher education: Evidence from Italy

Predicting university dropout is crucial. Identifying at-risk students can inform dropout prevention policies, safeguarding the nation’s resources and mitigating the long-term deterioration of human capital. In contrast to previous literature, this study prioritizes predicting student dropout rather than delving into causal mechanisms. This study leverages administrative data encompassing the entire population of Italian students enrolled in bachelor’s degree programs for the academic year 2013–2014. Our quantitative findings indicate that machine learning algorithms exhibit significant predictive capabilities, specifically random forest and gradient boosting machines, underscoring their potential as early warning indicators. Feature importance analysis emphasizes the role of students’ first-year academic performance in dropout prediction. Furthermore, our findings provide additional evidence regarding the influence of family income, high school grades, and high school type. The adoption of these novel predictive tools can facilitate the targeted implementation of policies aimed at mitigating this phenomenon.

Why English Isn't that Hard: Impact of Adopt-A-Friend Program on Students with Failing Grades

The purpose of this study is to narrate the conditions of selective grade 10 students at-risk of failing English 10. This also deals with assessing the impact of Adopt-a-Friend Program on their academic performance and socialization in class. The study used qualitative method triangulated through observation, structured written and oral interview, and used of existing documents (F138) and journal entry. Students with failing grades were purposively chosen and paired with high performing students in a peer-tutoring approach. Although the target of the program are selected students at-risk of failing English subject, it was launched to all grade 10 students to eliminate stereotyping since academic grades became the basis for choosing the respondents. The study revealed that those students at-risk of failing English 10 benefited much from the program. Most of the responses revealed that they have improved their academic performance and socialization in class. Moreover, they suggested that the program must continue until the last quarter of the school year. Those high performing students who adopted them revealed that they enjoyed the program, however they suggested some improvements for Adopt-a-Friend Program’s next implementation. As a result, there is a significant increase in the performance of the student adoptee and adopted.

Predictors of attrition and program dismissal in a nursing major

BackgroundIncreasing the representation of nurses and other health professionals from minority populations is one way to address health disparities. The purpose of this study was to investigate nursing admission criteria that predicted first, second, third semester attrition and program dismissal. MethodA retrospective secondary data analysis was conducted on existing student records spanning from 2016 to 2020 in one BSN program from a Hispanic-serving institution to gain an understanding about program attrition and dismissal. A total of n = 349 student records were included in the analysis. ResultsPre-nursing science GPA, pre-nursing college GPA, and reading ability were found significant predictors for attrition and dismissal, respectively. ConclusionsAptitudes in the sciences and in reading should be emphasized in pre-nursing to enhance student success in nursing. Findings from this study could inform administrators in postsecondary and in secondary education about establishing infrastructures for at-risk students to better prepare the latter for a nursing major.

Do Live Writing/ Study Sessions for At-Risk Students, in an Online, Distance Learning Environment, Demonstrate the Same Benefits Documented for Face-to-Face Shared Writing Events?

A recent project focussing on students studying an Arts and Humanities Access module at the Open University revealed that students in IMD Q1 are disproportionately more likely to ‘passively’ withdraw than their peers (i.e. not submit an assignment, not register with the university their intention to defer, ultimately leading to a ‘fail’ grade for that module: Lavery & Padley, 2022). Recent data shows a significant award gap for students studying Arts and Humanities modules who have a mental health need registered with the university. Colleagues were therefore motivated to assess whether the inclusion of synchronous, online, study and support opportunities, training students in the use of the Pomodoro technique, might support success for participants. This paper outlines the approach of two projects that applied techniques associated with group writing sessions, reflecting on whether their findings demonstrate similar benefits for participants in an online, distance learning environment as for those traditionally run in the physical space (i.e. writing retreats).

Applying Predictive Analytics using Clickstream Data for Improving the Students Performance

Student performance analysis is an essential aspect of educational institutions. In recent years, machine learning (ML) has emerged as a promising tool to analyze student performance. To predict learning abilities of students and prescribing them a personalized learning curriculum, it is necessary to estimate their behavior to know about their weaknesses, strengths and help institutions to improve enrollment and retention. If it is possible for the teachers to predict in advance and prescribe ways to the at-risk and dropout students, they can plan more effectively to help them. We are describing in this paper various intelligent tutoring systems with Educational Data Mining, Predictive Learning Analytics, prediction of at-risk students at an earlier basis and how this prediction task is done. Predictive Analytics can also offer insights to help students to make informed decisions about each individual student to improve outcomes by understanding what drives each student behavior and how much the institution can create intensional, specific plans that will positively impact students.

Gone with the weed: incidents of adolescent marijuana use in the United States, 1976–2021

This study sought to examine grouped and right-censored (GRC) counts of adolescent marijuana useand estimate its temporal trajectories and sociodemographic disparities over almost half a century. After compiling 46 waves of nationally representative data from the Monitoring the Future (MTF) study from 1976 to 2021 (sample size=491,348), we utilized an innovative modified Poisson (mixture) approach to analyze past-year marijuana use quantified by GRC counts. The overall reduction in incidence rates of marijuana use was attributable to an almost 40% reduction in the risk of marijuana use (with the proportion of at-risk adolescents at 51.36% in 1979 and 31.53% in 2021). Despite substantial changes over the study period, the recent incidence rates for at-risk individuals were similar to those in the early 1980s. Living in an intact family was a protective factor against adolescent marijuana use over time. The incidence rates of marijuana use among at-risk students, especially those from disadvantaged families, remained high over the study period. The modified Poisson (mixture) approach serves as the preferred tool for modeling GRC responses. It is essential to distinguish among risk, at-risk incidence, and overall incidence when assessing substance use and other risky behaviors.