In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying ...potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the established dropout risk prediction model to improve student learning effectiveness. The model was based on the academic portfolios of past students and built with statistical learning and deep learning methods. This study used this model to predict the dropout risk of 2205 freshmen enrolled in the fall semester of 2018 (graduated in June 2022) in the field of sustainable education. A total of 176 students with a dropout risk of more than 20% were considered high-risk students. After tracking and the appropriate guidance, the dropout risk of 91 students fell from > 20% to < 20%. To discuss the results from the perspective of gender and financial disadvantages, the improvement rate of the dropout risk for male students was 10.2% better than that of female students at 2.9%. The improvement rate in dropout risk for students with disadvantageous financial situations was as high as 12.0%, surpassing the 5.9% rate among general students. Overall, the dropout rate in the second year of the 2018 freshman cohort was lower than that of the 2016 and 2017 freshman cohorts. A predictive model established by statistical learning and deep learning methods was used as a tool to promote precision education, accurately and efficiently identifying students who are having difficulty learning, as well as leading to a better understanding of AI (artificial intelligence) in smart learning for sustainable education.
This article presents a fully integrated flipped voltage follower (FVF) based low-dropout (LDO) regulator with enhanced full-spectrum power supply rejection (PSR) and unity-gain bandwidth over 400MHz ...for noise-sensitive circuits. Following the study of three types of FVF LDO's PSR performances, we propose a novel FVF LDO with a low-gain fast loop-1 and a high-gain slow loop-2. In prior FVF LDOs, their PSRs are either full-spectrum, or not, but with low PSR at low frequency. In this article, we fully utilize both dc gains of loop-1 and loop-2 for the low-frequency PSR, while the high-frequency PSR remains unchanged. In addition, we use dynamic compensation to push the loop-2's UGB to higher frequency for a better PSR bandwidth. This work, fabricated in 65 nm complementary metal oxide semiconductor (CMOS), with 1.2-V input and 1-V output, exhibits a measured quiescent current ( I Q ) varying from 27 to 82 μ A for a load current I LOAD between 5 μ A and 20 mA. The circuit achieves a low frequency PSR of -58 dB with the worst full-spectrum PSR of -9 dB in 20 mA I LOAD with a 300 pF on-chip output capacitor. Further, with an UGB over 400 MHz, the proposed FVF LDO reaches 0.9 ns response time when I LOAD changes between 100 μ A and 20 mA with edge times less than 0.8 ns.
DROPOUT AND THERAPEUTIC ALLIANCE SHARF, JENNIE; PRIMAVERA, LOUIS H; DIENER, MARC J
Psychotherapy (Chicago, Ill.),
12/2010, Letnik:
47, Številka:
4
Journal Article
Recenzirano
This meta-analytic review of 11 studies examined the relationship between psychotherapy dropout and therapeutic alliance in adult individual psychotherapy. Results of the meta-analysis demonstrate a ...moderately strong relationship between psychotherapy dropout and therapeutic alliance (
d
= .55). Findings indicate that clients with weaker therapeutic alliance are more likely to drop out of psychotherapy. The meta-analysis included a total of 1,301 participants, with an average of 118 participants per study, a standard deviation of 115 participants, and a range from 20 to 451 participants per study. Exploratory analyses were conducted to determine the influence of variables moderating the relationship between alliance and dropout. Client educational history, treatment length, and treatment setting were found to moderate the relationship between alliance and dropout. Studies with a larger percentage of clients who completed high school or higher demonstrated weaker relationships between alliance and dropout. Studies with lengthier treatments demonstrated stronger relationships between alliance and dropout. Inpatient settings demonstrated significantly larger effects than both counseling centers and research clinics. No significant differences were found between client-rated, therapist-rated, and observer/staff-rated alliance. Recommendations for clinicians and researchers are discussed.
It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based ...interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.
A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.
Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender relative risk (RR) 1.08, lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).
Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.
Official sanctioning of students by the criminal justice system is a long-hypothesized source of educational disadvantage, but its explanatory status remains unresolved. Few studies of the ...educational consequences of a criminal record account for alternative explanations such as low self-control, lack of parental supervision, deviant peers, and neighborhood disadvantage. Moreover, virtually no research on the effect of a criminal record has examined the "black box" of mediating mechanisms or the consequence of arrest for postsecondary educational attainment. Analyzing longitudinal data with multiple and independent assessments of theoretically relevant domains, the authors estimate the direct effect of arrest on later high school dropout and college enrollment for adolescents with otherwise equivalent neighborhood, school, family, peer, and individual characteristics as well as similar frequency of criminal offending. The authors present evidence that arrest has a substantively large and robust impact on dropping out of high school among Chicago public school students. They also find a significant gap in four-year college enrollment between arrested and otherwise similar youth without a criminal record. The authors also assess intervening mechanisms hypothesized to explain the process by which arrest disrupts the schooling process and, in turn, produces collateral educational damage. The results imply that institutional responses and disruptions in students' educational trajectories, rather than social-psychological factors, are responsible for the arrest-education link.
The transition from school to university mathematics is a challenging process for many students. This phenomenon is reflected by high dropout rates from mathematics programs especially during the ...first year at university that may be related to the development of students’ mathematical interest and self-concept. Taking a learning psychological perspective, it is a relevant question if all students have similar development patterns of these variables or if students who are at risk to drop out show an unfavorable development. Moreover, for didactical issues, it is of particular relevance which factors influence this development. Within a longitudinal study, 556 first-semester students stated their interest and self-concept at the beginning and the middle of the first semester; dropout was measured at the beginning of the second year. By using regression analyses, we found that the development of students’ interest and self-concept is related to dropout. It seems that interest in university mathematics and mathematical self-concept of students, who dropped out, decreased significantly in contrast to the characteristics of students who continued their studies. In an exploratory follow-up study, we identified factors which probably influenced the development of students’ self-concept by asking 18 students in a first-semester mathematics course “linear algebra” about learning situations in which they experienced the emergence or the decline of self-concept. Using qualitative content analysis, we found that most of these situations occurred when students worked on mathematical homework tasks or got criterial feedback on their solutions. Based on the presented results, we discuss implications for teaching and learning of undergraduate mathematics.
•In a community-based RCT, 58% of clients completed all 12 sessions of CPT.•Most treatment noncompletion occurred between sessions two and five.•Clients who improved at a greater rate attended more ...sessions.•The average client needed to attend at least 10 sessions for favorable outcomes.
Trauma-focused therapies, including Cognitive Processing Therapy (CPT; Resick et al., 2016), are effective at reducing clients’ PTSD symptoms. A limitation to these treatments, however, is client completion of them. The current study examined temporal patterns of treatment non-completion and the relationships among non-completion, PTSD, and overall mental health functioning outcomes, among clients in a randomized controlled CPT implementation trial. Two models of symptom change were tested: 1) dose-effect model (i.e., clients uniformly improve with additional sessions at a negatively accelerating rate); and 2) the good-enough level model (i.e., clients remain in therapy until they have achieved sufficient improvement, thus clients who attend fewer sessions improve at quicker rates). Results indicated that 42% of clients did not complete treatment, with most discontinuing between sessions two and five. Data did not fit the dose-effect or good-enough level model. Rather, clients who improved at a greater rate in their PTSD symptoms and overall mental health functioning attended more sessions. The average client had the best outcomes when they completed all 12 sessions. Identifying clients who may be at risk for discontinuing treatment, and making a concerted effort toward retaining them, is imperative to reduce non-completion rates and ultimately improve client outcomes.
Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors - including student mental health - ...remains poorly understood.
Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope - for example, they often rely on small and/or non-representative samples or subjective measures, and focus
almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities
between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3
(adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the
student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention.
Author abstract
Dropout is a ubiquitous psychotherapy outcome in clinical practice and treatment research alike, yet it remains a poorly understood problem. Contemporary dropout research is dominated by models of ...prediction that lack a strong theoretical foundation, often drawing on data from clinical trials that report on dropout in an inconsistent and incomplete fashion. In this article, we assert that dropout is a critical treatment outcome that is worthy of investigation as a mechanistic process. After briefly describing the scope of the dropout problem, we discuss the many factors that limit the field’s present understanding of dropout. We then articulate and illustrate a transdiagnostic conceptual framework for examining psychotherapy dropout in contemporary research, concluding with recommendations for future research. With a more comprehensive understanding of the factors affecting retention, research efforts can shift toward investigating key processes underlying treatment dropout, thus, boosting prediction and informing strategies to mitigate dropout in clinical practice.
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