No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University
Abstract
This paper focuses on university-level education offered by methods of distance learning in the field of computers and aims at the investigation of the main causes for student dropouts. The presented study is based on the students of the Course of “Informatics”, Faculty of Science and Technology of the Hellenic Open University and investigates the particularities of education provided through the use of computers and technology in general. This paper presents information about the students' profile, the use of computer technology, the percentage of dropouts, as well as a classification of the reasons for dropouts based on interviews with the students. The study shows that dropouts are correlated with the use of technological means and, based on this fact, the Hellenic Open University implemented interventions in the use of such means. It also proves that a correlation exists between dropouts and students' age, but not gender, although female students are more reluctant to start following a course. However, it is also shown that female students' commitment to a course is stronger and thus, they do not drop out as easily as male students do. Furthermore, the results of this study strongly correlate dropouts to the existence of previous education in the field of Informatics or to working with computers, but not to the degree of specialisation in computers. Finally, the paper presents the reasons provided by the students for drooping out, with the main reasons being the inability to estimate the time required for university-level studies and the perceived difficulty of the computers course.
... A desistência na EaD ocorre por motivos distintos e a evasão pode ser ocasionada por motivos pessoais ou pela ausência de infraestrutura tecnológica, como acesso a internet tecnologias, sociedade e conhecimento vol. 10, n. 2, dez./2023 e aos recursos computacionais (Xenos;Pierrakeas;Pintelas, 2002 ...
... A desistência na EaD ocorre por motivos distintos e a evasão pode ser ocasionada por motivos pessoais ou pela ausência de infraestrutura tecnológica, como acesso a internet tecnologias, sociedade e conhecimento vol. 10, n. 2, dez./2023 e aos recursos computacionais (Xenos;Pierrakeas;Pintelas, 2002 ...
... A desistência na EaD ocorre por motivos distintos e a evasão pode ser ocasionada por motivos pessoais ou pela ausência de infraestrutura tecnológica, como acesso a internet tecnologias, sociedade e conhecimento vol. 10, n. 2, dez./2023 e aos recursos computacionais (Xenos;Pierrakeas;Pintelas, 2002 ...
Este trabalho é um estudo de caso exploratório com abordagem qualitativa que analisou a relação entre o uso de recursos educacionais em ambientes virtuais de aprendizagem, na Educação Superior a distância, e as taxas de evasão. O estudo realizado justifica-se em virtude da terceira maior causa da evasão, originária dentro do próprio sistema educacional, estar relacionada com o uso da plataforma. O uso de Tecnologias Educacionais, síncronas e assíncronas, pode influenciar nos índices de evasão. Os resultados indicam que cursos com mais recursos tiveram maiores taxas de evasão, enquanto aqueles com menos recursos tiveram taxas menores. Por isso, apresenta-se uma análise crítica dos resultados da pesquisa, destacando as implicações e as contribuições do estudo.
... Previous works investigated reasons for student drop-out in specific courses [30,61] or barriers to entrance and retention in computing courses in narrower contexts [33], a single institution [5,8,29,68] or retention in a group of institutions in the US [62]. However, there is little research investigating a broader national context that explores retention and drop-out reasons, particularly in Latin America. ...
... Xenos et al. [68] discuss drop-out rates and their causes in the Informatics program of the Hellenic Open University. The courses are not face-to-face, thus their results are influenced by distance learning factors. ...
... It is important to see "the big picture", so we need to help students see it. These findings are consistent with other studies, like [68], which identify a lack of previous knowledge about computing as one of the main drop-out reasons. We believe that the introduction of CS principles in schools, besides being extremely important for shaping the citizen of the 21st century, will provide students with the necessary background to make a conscious choice when engaging in a CS degree program, reducing drop-out rates. ...
Motivation : Enrollments in Brazilian computing degrees are at an all-time high, but graduation numbers have not increased at the same rate. Moreover, enrollment growth has mainly attracted male students, steadily expanding the gender gap in Brazilian computing programs. Such high attrition rates have a great economic impact and may disproportionately affect women and students of color. Previous works investigated reasons for student drop-out and retention in specific courses or barriers to entrance in computing programs in narrower contexts or in a single institution. Objectives : We investigate potential actionable factors for intent to drop out in computing programs and what factors might lead students to remain in a computing program in several Brazilian institutions. We investigated how such factors may be affected by students’ race/ethnicity, gender, and socioeconomic status. Method : We analyzed Likert-style answers from an online survey with 3193 students currently enrolled in Brazilian computing programs. Results : The results show that students value salary/job-related factors as the most important factors to potentially remain in a computing program. The excess of theoretical courses and the difficulty of programming and mathematics courses are the top-ranked factors by students to potentially abandon a computing degree. However, while there is little effect of gender, race/ethnicity, or socioeconomic status in retention factors, potential drop-out factors such as a male-dominated field, harassment, and the difficulty of courses were rated as more important by women. Also, costs and the difficulty of courses are relevant factors for the intent to drop-out when analyzing students’ race/ethnicity and socioeconomic status. Discussion : We explore the implications of our findings for Computing programs, particularly (but not restricted to) the Brazilian context. We conjecture reasons for such students’ perspectives regarding intent to drop-out and retention factors and provide recommendations of actions for instructional designers, curriculum developers, and other key stakeholders to address issues related to gender, students’ wellness, perceived authenticity of courses, and other relevant factors. Since we only observed small interactions between race/ethnicity and retention and intent to drop-out factors, which may indicate a lack of sensitivity from the instrument, we lay suggestions to address such limitations in future work.
... Some researchers reported no noteworthy difference in the age of students who drop out from online courses (Levy, 2007;Tello, 2008;Willging and Johnson, 2009;James et al., 2016), while others have noted age as an important factor (Xenos al., 2002;Pierrakeas et al., 2004;Wladis et al., 2015;Murphy and Stewart, 2017). It has been posited that older students tend to drop out and require more encouragement from their teachers (Xenos et al., 2002). Conversely, a retention study for online (STEM) courses reveals that older students showed better performance and had more likelihood of persistence (Wladis et al., 2015). ...
... While examining past educational and professional experiences of learners enrolled in an Informatics course online, Xenos et al. (2002) discovered that learners with prior backgrounds in programming or data handling showed significantly higher persistence rates. However, Cheung and Kan (2002) have not found previous experiences significant in persistence/ dropout decisions. ...
... The skill and ability to balance multiple responsibilities have been seen in those learners who complete their online courses (Müller, 2008;Joo et al., 2011). Realistic expectations about the time and effort to complete a task are reported to facilitate better academic performance and completion of online courses (Xenos et al., 2002;Wladis et al., 2015). ...
Online learning is becoming more popular with the maturity of social and educational technologies. In the COVID-19 era, it has become one of the most utilized ways to continue academic pursuits. Despite the ease and benefits offered by online classes, their completion rates are surprisingly low. Although several past studies focused on online dropout rates, institutions and course providers are still searching for a solution to this alarming problem. It is mainly because the previous studies have used divergent frameworks and approaches. Based on empirical research since 2001, this study presents a comprehensive review of factors by synthesizing them into a logically cohesive and integrative framework. Using different combinations of terms related to persistence and dropout, the authors explored various databases to form a pool of past research on the subject. This collection was also enhanced using the snowball approach. The authors only selected empirical, peer-reviewed, and contextually relevant studies, shortlisting them by reading through the abstracts. The Constant Comparative Method (CCM) seems ideal for this research. The authors employed axial coding to explore the relationships among factors, and selective coding helped identify the core categories. The categorical arrangement of factors will give researchers valuable insights into the combined effects of factors that impact persistence and dropout decisions. It will also direct future research to critically examine the relationships among factors and suggest improvements by validating them empirically. We anticipate that this research will enable future researchers to apply the results in different scenarios and contexts related to online learning.
... Several factors may influence dropout [3], the main ones reported in the literature related to financial and family reasons, unfulfilled expectations and lack of motivation [5]. Xenos, Pierrakeas and Pintelas [40] stated that the identification of such specific factors is essential to provide special assistance to students, and categorizes them as related to: (i) internal or students' perception; (ii) course and professors and; (iii) student demographic characteristics. ...
... Previous studies found that college attrition depend on the type of course [40,8], student's year at college [8,36,18,19], social issues such as parental background [2,8], class-cultural discontinuities [17] and economic profile [28]; as well as quantitative academic data [25]. ...
College dropout is a concern for educational institutions since it directly impacts educational management and academic results, as well as being directly related to social problems. Therefore, there is significant incentive for studies that use data to support decisions by predicting risk of dropout so that institutions can attempt to prevent such cases. Although machine learning techniques were shown to have potential for this task, there are many steps involved when it comes to the use of real data, which comes from scattered systems and present issues such as need for data cleaning and preparation, high dimensionality of the data requiring adequate feature selection, as well as class imbalance. In this paper, we used data from 32.892 students enrolled between 2008 and 2020 from all courses offered by a public high-education institution. A protocol for data preparation is proposed and found to be more important than designing complex classifiers. We present guidelines when modelling a college dropout classification task using a public university data and experiments using Walk-Forward Validation that showed the predictive capacity for the first years.
... Student attrition affects institutions of higher learning globally and can be damaging to their reputation [5]- [7]. Several significant reasons have emerged for the high rate of attrition within CS: poor project management skills, lack of understanding of the material, not identifying with the career path, cultural issues, lack of assistance and feedback, poorly designed courses, personal problems, and more [8]- [10]. As a result, CS education researchers have extensively studied this problem, using various tools and techniques to make early interventions before students drop out. ...
... Although there are other highly ranked journal outlets focusing on technology in education, for example, the IEEE Transactions on Learning Technologies (IF = 4.43 as at 2022) and Education and Information Technologies (IF = 3.67 as at 2022), which falls on number 9 and 10 respectively as shown in the result. One insight to draw from this finding is that studies on attrition is of high quality and thus, their contribution to knowledge [10]. On the other hand, it may be impossible to generalize that all attrition studies are published by highly ranked journals since there could be articles that may be published in outlets not indexed in Scopus or Web of Science. ...
Student attrition is a long-standing problem in Computer Science (CS), as in many other disciplines, and it has gained momentum in the academic sphere. This study employs
bibliometric analysis
to shed light on the research stream of student attrition within CS. Bibliometric analysis is a popular technique for evaluating published scientific articles when empirical contributions are producing voluminous research streams. We collected 1310 articles from the Web of Science and Scopus databases, published over a period of 22 years from 2000 to 2022, to analyze the most relevant publication venues in the study of attrition in CS. Further analysis revealed the most cited institutions, countries, key themes, and other conceptual information. Keywords, such as “retention,” “computer science education,” “gender,” “introductory programming,” and “student success” emerged as dominant themes in attrition studies. As researchers work intensively to reduce attrition within CS, these thematic areas may continue to shape the future direction of attrition studies. Our study provides a comprehensive overview of research hotspots, thematic areas, and future directions for attrition studies in CS. This outcome could be valuable for young and emerging scholars who are starting their careers and looking to identify research hotspots in this field of interest.
... Breve análise da literatura Para Xenos et al. (2002), a evasão, principal preocupação de instituições de ensino a distância, é causada por múltiplos fatores endógenos e exógenos ao curso. Há alguns resultados relevantes de pesquisas, citados por esses autores, indicando que um dos principais fatores que afetam a evasão é a quantidade de módulos completada pelo aluno. ...
... Os cursos de extensão e especialização têm 25% de evasão. Xenos et al. (2002) mostrou, através de seus estudos, que entre os fatores internos como explicativos de evasão, estão: a percepção de dificuldade do curso, a motivação, a persistência do aluno e seu locus de controle. Resultados interessantes, porém não conclusivos, mostram que as mulheres tendem a persistir mais do que os homens nos cursos. ...
Este trabalho apresenta os resultados de pesquisa qualitativa sobre evasão em cursos pela Internet, trabalhando com um caso específico, o curso “Aprender a Empreender pela Internet”, oferecido pelo SEBRAE Nacional. O projeto analisa os principais problemas da evasão, tanto baseada em referencial teórico estudado, quanto sob a ótica dos próprios alunos. Analisa também as características dos alunos, as técnicas utilizadas pelos tutores, destacando as que surtem maior resultado. Por fim, é dedicada atenção aos depoimentos dos alunos para buscar instrumentos que possibilitem os tutores a exercer esta ação educativa tão importante na atual Sociedade da Informaçãoe do Conhecimento.
... Several studies show that early dropouts in university years is not conjectural. They are influenced by several factors such as internal factors related to the student, the lecturer, and academic tutors, and factors relating to the demographic of the student [59]. Internal factors such as perceptions of course difficulty and the motivational and persistence level are abstract and difficult to measure. ...
... These analyses reinforce the conclusions that age has some significant influence on the dropout rate [59,60], that the economic environment in the community or area can mitigate or exacerbate the risk of dropping out [61,62], and that scholarships are essential, especially for students with low resources [63,64]. However, some counterfactuals are not actionable. ...
High and persistent dropout rates represent one of the biggest challenges for improving the efficiency of the educational system, particularly in underdeveloped countries. A range of features influence college dropouts, with some belonging to the educational field and others to non-educational fields. Understanding the interplay of these variables to identify a student as a potential dropout could help decision makers interpret the situation and decide what they should do next to reduce student dropout rates based on corrective actions. This paper presents SDA-Vis, a visualization system that supports counterfactual explanations for student dropout dynamics, considering various academic, social, and economic variables. In contrast to conventional systems, our approach provides information about feature-perturbed versions of a student using counterfactual explanations. SDA-Vis comprises a set of linked views that allow users to identify variables alteration to chance predefined students situations. This involves perturbing the variables of a dropout student to achieve synthetic non-dropout students. SDA-Vis has been developed under the guidance and supervision of domain experts, in line with some analytical objectives. We demonstrate the usefulness of SDA-Vis through case studies run in collaboration with domain experts, using a real data set from a Latin American university. The analysis reveals the effectiveness of SDA-Vis in identifying students at risk of dropping out and proposes corrective actions, even for particular cases that have not been shown to be at risk with the traditional tools that experts use.
... p < 0.001), meaning that older students tend to graduate faster. This finding coincides with the findings of Xenos et al. (2002) and Whiteman (2004), where student attrition rates in relation to first-year students' age were discussed. ...
Using a structural equation modeling approach, this study has attempted to untangle the underlying pathways on how students’ demographics and pre-college characteristics that reflect academic preparation, combined with major factors formulated in the university environment, affect time to degree. It does so by developing and evaluating a conceptual framework whereupon time to degree is associated with specific observed or latent factors. A properly tailored Multiple Indicator Multiple Causes SEM was used for evaluating the hypotheses made on a sample of 1137 graduates which came from a Greek University of Social and Political Science, Athens, Greece. AMOS and LISREL packages were used for the analysis. The results reveal interesting direct and indirect relationships of the various predictor variables with time to degree. In particular, the great contribution of student performance and academic integration to time to graduation has been highlighted. However, the contribution of the pre-university features is also worthy of attention.
... As reported by Xenos et al. (2002), it is a significant objective to prevent or reduce the rate of dropouts in distance education. Predictive data mining and machine learning models have often been utilized in student dropout prediction in distance education. ...
Speed-reading courses are designed to improve their students’ reading speed and comprehension. The use of e-learning environments enables data collection that helps in the assessment of students and predictive analyses. Student dropout prediction is among the popular problems in this context. This study presents a neural network-based prediction model that identifies impending dropouts from a speed-reading course. Assessment scores obtained in the last ten sections were analyzed to predict dropouts who will not proceed to the next level. Despite the challenge of predicting short-term dropouts, the tests resulted in an accuracy of 78.24% on average. Moreover, 56.58% of predicted students dropped out before the next level, while 52.67% of students were successfully identified just before the dropout.
... As reported by Xenos et al. (2002), it is a significant objective to prevent or reduce the rate of dropouts in distance education. Predictive data mining and machine learning models have often been utilized in student dropout prediction in distance education. ...
Günümüz dünyasında bilgi ve teknoloji, kurumların başarısında belirleyici bir rol oynamaktadır. Yönetim Bilişim Sistemleri (YBS), bu başarının temel taşı olarak, verinin üniteler arasında etkin bir şekilde paylaşılmasını, analiz edilmesini ve anlamlı kararlara dönüştürülmesini sağlamaktadır. Dijital dönüşümün hız kazandığı bu çağda, Yönetim Bilişim Sistemleri üzerine yapılan güncel çalışmalar, kurumların stratejik hedeflerine ulaşması için çok önemli bir rehber niteliği taşımaktadır. “Yönetim Bilişim Sistemlerinde Güncel Uygulamalar” adlı bu eser, akademisyenler, öğrenciler ve sektör profesyonelleri için kapsamlı bir kaynak sunmayı amaçlamaktadır. Kitap, hem teori hem de uygulama alanındaki son gelişmeleri bir araya getirerek okuyuculara yenilikçi bir bakış açısı kazandırmayı hedeflemektedir. Veritabanı yönetimi, bulut bilişim, yapay zeka destekli yönetim sistemleri ve büyük veri analitiği gibi çeşitli konular, kitabın temel başlıklarını oluşturmaktadır. Bu eser, sadece teknolojik gelişmeleri ele almakla kalmayıp, aynı zamanda bu gelişmelerin kurumsal stratejilere entegrasyonu, çalışan verimliliğinin artırılması ve karar alma süreçlerine olan katkısını da detaylı bir şekilde irdelemektedir. Alanın önde gelen isimlerinin katkılarıyla hazırlanan bu kitap, YBS alanında yeni ufuklar açmak isteyenler için hem teorik hem de pratik çözümler sunmaktadır. “Yönetim Bilişim Sistemlerinde Güncel Uygulamalar” kitabının okuyuculara ilham vermesi ve bu alandaki bilgi birikimine değerli katkılar sağlaması dileğiyle...
... As reported by Xenos et al. (2002), it is a significant objective to prevent or reduce the rate of dropouts in distance education. Predictive data mining and machine learning models have often been utilized in student dropout prediction in distance education. ...
Günümüz dünyasında bilgi ve teknoloji, kurumların başarısında belirleyici bir rol oynamaktadır. Yönetim Bilişim Sistemleri (YBS), bu başarının temel taşı olarak, verinin üniteler arasında etkin bir şekilde paylaşılmasını, analiz edilmesini ve anlamlı kararlara dönüştürülmesini sağlamaktadır. Dijital dönüşümün hız kazandığı bu çağda, Yönetim Bilişim Sistemleri üzerine yapılan güncel çalışmalar, kurumların stratejik hedeflerine ulaşması için çok önemli bir rehber niteliği taşımaktadır. “Yönetim Bilişim Sistemlerinde Güncel Uygulamalar” adlı bu eser, akademisyenler, öğrenciler ve sektör profesyonelleri için kapsamlı bir kaynak sunmayı amaçlamaktadır. Kitap, hem teori hem de uygulama alanındaki son gelişmeleri bir araya getirerek okuyuculara yenilikçi bir bakış açısı kazandırmayı hedeflemektedir. Veritabanı yönetimi, bulut bilişim, yapay zeka destekli yönetim sistemleri ve büyük veri analitiği gibi çeşitli konular, kitabın temel başlıklarını oluşturmaktadır. Bu eser, sadece teknolojik gelişmeleri ele almakla kalmayıp, aynı zamanda bu gelişmelerin kurumsal stratejilere entegrasyonu, çalışan verimliliğinin artırılması ve karar alma süreçlerine olan katkısını da detaylı bir şekilde irdelemektedir. Alanın önde gelen isimlerinin katkılarıyla hazırlanan bu kitap, YBS alanında yeni ufuklar açmak isteyenler için hem teorik hem de pratik çözümler sunmaktadır. “Yönetim Bilişim Sistemlerinde Güncel Uygulamalar” kitabının okuyuculara ilham vermesi ve bu alandaki bilgi birikimine değerli katkılar sağlaması dileğiyle...
... The data collection instruments were the teacher survey and the information provided by this institutional unit. Finally, the scientific article was developed based on the following sections: Introduction, literary review, results, discussion, conclusions, limitations and recommendations (Olaya et al., 2020;Xenos et al., 2002;Wild & Heuling, 2020;Krüger et al., 2023). ...
The investigation adopted a mixed approach (qualitative and quantitative) in order to analyze the different factors of dropout. Based on the analysis of the socioeconomic factors that constitute an element that affects student dropout at the Eloy Alfaro Lay University of Manabí ext. Chone. The methods used were: Bibliographic, analytical/synthetic, inductive/deductive, documentary, and descriptive. The documentary method contributed to collecting information from the Academic Commission and Secretariat of this institution that contributed to supporting the research. The results showed that the main causes for student dropout are lack of time, work impediments, health problems, and economic situation. In the period from 2020 to 2023, 509 students were registered, of which 446 students graduated in different careers. According to the information collected through the teacher survey, it was determined that the main reasons for student dropout are linked to the lack of economic resources, work aspects, and family problems. At the level of conclusions and based on the data obtained, it was possible to demonstrate that socioeconomic factors constitute an element that affects student dropout. Factors such as academic, economic, family, intention to drop out, motivational, health, and social factors can influence university student dropout.
... En otros estudios, la formación previa en matemáticas relacionada con los puntajes de las pruebas de ingreso y el rendimiento académico del primer año, particularmente en matemáticas introductorias e informática, se ha identificado como predictor de deserción (Kori et al., 2018;Niitsoo et al. 2014). De igual forma, se analizaron variables no evaluadas en los estudios anteriores, tales como conocimientos teóricos y prácticos de informática (Araque et al., 2009;Xenos et al., 2002) y desarrollo del pensamiento lógico y analítico (Salazar-Fernández et al. 2019;Mitic et al. 2021). Si bien se examinaron variables socioeconómicas, ninguno de estos estudios utilizó un enfoque económico. ...
Un tema preocupante en la Carrera de Ingeniería Civil de la Facultad de Ciencias Exactas y Tecnología de la Universidad Nacional de Tucumán (FACET-UNT) es el abandono estudiantil. En el periodo que va desde 2005 hasta 2019, un total de 900 alumnos dejaron la carrera, de un total de 1615 ingresantes. El presente estudio se basó en el análisis de datos de historias académicas de estudiantes de ingeniería civil en la FACET-UNT. Nuestro objetivo principal fue determinar las variables de rendimiento académico que tienen un impacto significativo en la deserción de la carrera. Para ello implementamos un modelo de correlación utilizando LightGBM (Barbier et al.2016;Keet al.2017; Shi et al.2022). Utilizamos este modelo para identificar las variables claves que influyen en la probabilidad de deserción de los estudiantes. Además, empleamos la teoría de juegos para interpretarlos resultados obtenidos. Específicamente, utilizamos la biblioteca SHAP (Lundberget al.,2018, 2020; Lundberg & Lee,2017) en Python para calcular los números de Shapley. Los resultados de nuestro estudio revelaron las variables más importantes que influyen en el abandono. Se identificaron diferencias significativas en términos de edad, tiempo transcurrido en los estudios y rendimiento académico, que incluye la cantidad de cursos aprobados y el número de exámenes realizados. Estos resultados pueden ser de utilidad para desarrollar estrategias más efectivas de retención estudiantil y mejorar el éxito académico en esta disciplina.
... The study reveals a gender disparity in the likelihood of abandoning higher education, with males being more likely to drop out than females. It aligns with global trends where male students often show higher dropout rates, possibly due to societal expectations and pressures to join the workforce early, as noted by researchers [41]. In the Estonian context, it might reflect cultural attitudes towards gender roles and education, emphasising the need for targeted interventions to support specific students. ...
This study examines the relationship between various demographic and situational factors and working students’ decisions to change their study programmes and abandon higher education. It utilises a sample of 1902 working students derived from the Eurostudent VII survey and employs cross-tabulation and chi-square tests. The findings reveal statistically significant associations between several factors and students’ educational decisions. Males are more likely to consider abandoning higher education than females. Younger students, particularly those up to 21 years old, are more inclined to consider changing their study programmes. Financial difficulties significantly influence students’ considerations of both changing study programmes and abandoning higher education. Students in the arts, humanities, and ICT are more likely to consider abandoning their studies. Conversely, age does not significantly affect the likelihood of abandoning higher education. Parental educational attainment does not significantly influence decisions to change or abandon study programmes, whereas living situations, such as living independently and not living with parents, significantly affect changing the study programme. Qualification level affects the likelihood of changing study programmes, with bachelor’s students more likely to consider changes than masters and long-term national degree students, but it does not significantly affect the likelihood of abandoning higher education. Education–job mismatch significantly affects both changing study programmes and abandoning higher education, while the duration of working hours only influences the decision to alter study programmes. By revealing these findings, this research extends the student retention discourse as well as highlights how cultural, economic, familial, and workplace capital influence working students’ educational decisions.
... Timely and effective feedback provided to students on their submitted work has the potential to significantly enhance learning, improve student self-efficacy, reduce drop-out rates, and improve educational outcomes, especially among undergraduates in the early stages of their academic program [1], [2]. In introductory computer programming courses, where inordinately high dropout rates have been reported [3]- [5], early feedback can play a vital role in supporting student success and retention. Computer systems capable of automatic grading and feedback generation have gained considerable traction over the past two decades. ...
... It is worthy of mention that research in IT and CS uncovers several factors having influence on students' behaviour in their studies or retention in IT or CS education; these factors include demographics (e.g., gender, year of studies, race), motivation, family support, academic aspects (e.g., difficult learning material, mathematics, demanding examinations), learning environment (e.g., studentstudent interaction, student-faculty interaction, classroom climate), beliefs, and cultural stereotypes [ [44][45][46][47][48][49]50]]. Although these studies provide some understanding of IT or CS retention, the need for further studies comes from the lack of research specifically probing into the study skills needed in these two disciplines. ...
Not a great deal is known about what study skills are essential for success in the science, technology, engineering, and mathematics' disciplines, particularly in information technology (IT) and computer science (CS) programs in the technical and vocational education (TVE) in Taiwanese higher education. Since TVE accounts for more than half of the post-secondary enrolments in the country, and with the increasing demand for IT and CS talents, we studied perceptions of the importance and competency of such skills to identify the students’ needs and demographic factors influencing them. A survey was administered to 1398 students in IT and CS programs in Taiwanese TVE universities. General skills were seen as the most important one among the study skills examined, and students felt competent using them. The needs to manage time, perform quantitative/mathematical tasks, and delegate were identified and these needs were affected by institutional quality, gender, and academic achievement. The results might be useful for further investigation in this area and guiding future plan to improve student performance in TVE.
... program 1 (Heublein et al., 2020). Reasons for dropout can be manifold and include, for example, performance and financial problems (Heublein et al., 2017), as well as family-or health-related reasons (Xenos et al., 2002). In addition, motivational factors also play a major role in the decision to leave higher education (e.g., Heublein et al., 2017). ...
Autonomous motivation for self-set goals (pursuing goals for more intrinsic and less extrinsic reasons) has been linked to affective well-being. Using intensive longitudinal data, the present article examines the link between university students’ autonomous study motivation with affective well-being and targets fulfillment of the basic psychological needs as a potential mediating factor of this association on the within-person and the between-person level. University students in Germany (N = 488) completed an online questionnaire once a week over up to two semesters, indicating their weekly study motivation, need fulfillment, and affective well-being. Multilevel structural equation models were employed to target (a) the structure of autonomous study motivation and (b) the associations of autonomous study motivation with need fulfillment and affective well-being. Autonomous study motivation was associated with students’ positive and negative affect on both levels. In line with predictions by self-determination theory, multilevel mediation models suggested indirect effects via need fulfillment on both levels. Results highlight the central role of autonomous study motivation and need fulfillment in university students’ socioemotional adjustment. A better understanding of this socioemotional adjustment of university students may be an important step for increasing overall study satisfaction and developing interventions to reduce study dropout.
... Sin embargo, es notable que ciertos estudiantes que utilizan tanto la computadora como el correo electrónico en casa prefieren enviar sus asignaciones por escrito por correo institucional. Esto es en parte debido al hecho de que la presentación por correo se considera más "oficial" que por mensajería instantánea (Xenos et al., 2002). ...
Esta obra colectiva corresponde al trabajo investigativo de un equipo de docentes peruanos quienes se propusieron establecer el nivel de actitud de los estudiantes de una universidad de Lima frente a la enseñanza-aprendizaje virtual durante la pandemia de COVID-19. Muchas instituciones educativas de todos los niveles, a lo largo y ancho de América Latina, optaron por incluir en sus procesos de enseñanza-aprendizaje las metodologías virtuales como estrategias favorables. Hablando de la educación superior, el reto fue importante en términos de mantener la calidad con buena cobertura, sosteniendo las exigencias académicas ya iniciadas y mejorando las plataformas digitales, con mayor accesibilidad a las mismas.
... In Greece, research on mature students in universities and the difficulties they face has not yet started. Several studies have been carried out only at the Hellenic Open University, which focuses mainly on the problems that adult students face in the context of their studies, and which are often a reason for abandoning their studies (Ginou, 2001;Pisli et al., 2022;Pierrakeas et al., 2004;Vergidis & Panagiotakopoulos, 2002;Vergidis & Panagiotakopoulos, 2003;Xanthopoulou & Stavrakakis, 2019;Xenos et al., 2002). ...
The aim of our study is to highlight the characteristics of mature students who study in person in Greek universities , the difficulties they face and the strategies they use to overcome them. The research was conducted through semi-structured interviews with a sample of 10 students of the Faculty of Social Sciences and Humanities of a regional Greek university and the data were analyzed using Pierre Bourdieu's theoretical framework. The results of the research showed that the characteristics that differentiate mature students from university students of typical age (18 - 22 years old) are related to adulthood. They enroll at university mainly to increase their institutional and symbolic capital, while the obstacles they face are mainly related to their family and professional obligations. They also develop specific strategies to achieve their goals. Finally, the Greek university needs to focus on this particular group of students and formulate a specific policy to contribute to addressing their problems.
Article visualizations:
</p
... Numerous efforts have been made to improve predictive power in this multidisciplinary research field. However, most dropout predictions focus on between-semester dropouts or week-to-week MOOC dropouts [13,29]. MOOCs represent an entire course and extend over several weeks with limited interaction capabilities. ...
Dropout prediction is an essential task in educational Web platforms to identify at-risk learners, enable individualized support, and eventually prevent students from quitting a course. Most existing studies on dropout prediction focus on improving machine learning methods based on a limited set of features to model students. In this paper, we contribute to the field by evaluating and optimizing dropout prediction using features based on personal information and interaction data. Multiple granularities of interaction and additional unique features, such as data on reading ability and learners’ cognitive abilities, are tested. Using the Universal Design for Learning (UDL), our Web-based learning platform called I3Learn aims at advancing inclusive science learning by focusing on the support of all learners. A total of 580 learners from different school types have used the learning platform. We predict dropout at different points in the learning process and compare how well various types of features perform. The effectiveness of predictions benefits from the higher granularity of interaction data that describe intermediate steps in learning activities. The cold start problem can be addressed using assessment data, such as a cognitive abilities assessment from the pre-test of the learning platform. We discuss the experimental results and conclude that the suggested feature sets may be able to reduce dropout in remote learning (e.g., during a pandemic) or blended learning settings in school.KeywordsDropout predictionScience EducationInclusion
... The comparison on performance is given in figure 7. Table 4 shows the comparison of student dropout finding and its causes. [31] An analysis of the student dropout rates and factors contributing to them during the informatics course at the Hellenic Open University incorrect time estimates for the students' jobs, which reduces the amount of time available for studying, lack of assistance from the tutor or less assistance than the student had initially anticipated., death of a family member, Health issues involving the student or another family member of the student, other factors besides those already mentioned that students classified as personal and refused to discuss with the interviewer. Pierrakeas et. ...
... The present study focuses on the dropout rate and reasons for student dropout as a performance criterion of the institute. Xenos et al. (2002) investigate the dropout in university-level education relevant to distance learning education at the Hellenic Open University. Results showed that dropouts are correlated to the use of technological means. ...
... The estimated parameter value of −0.1082 indicates that students who is 45 years old or above at risk of having low learning retention of 0.8975 times compared to other generations. This condition is following the study of Xenos et al. [62] and Pierrakeas et al. [44] in Greece as well as Schuemer [51] and Rovai [49]. Rovai [49] states that one of the reasons for dropping out of college at ODE is old age. ...
The problem that arises in the Cox model is that there are more than two types of covariates and the presence of random effects is a non-proportional hazard (NPH). One example of a case that involves many factors is student retention. Low student retention can lead to dropping out of college or failure in completing studies. The purpose of this study is to overcome the problem of NPH caused by the presenceof time-independent covariates, time-dependent covariates, and random effects. The research method uses simulation. Some of the modified models are the stratified Cox model, the extended Cox model, and the frailty model. The developed model is applied to distance education student retention data. The results of the study show that frailty and study programs provide considerable diversity in explaining thetotal diversity of the model. It can be concluded that frailty needs to be considered by UT to improve the quality of services to students. In addition, other covariates that have a significant effect on UT student learning retention modeling are age, domicile, gender, GPA, marital status, employment status, number of credits taken, and number of registered courses.
... In general, dropout is caused by professional, academic, health, family, behavior, and individual reasons (Xenos, Pierrakeas, & Pintelas, 2002;Kotsiantis, Pierrakeas, & Pintelas, 2003;Mubarak, Cao, & Zhang, 2022). Specifically, some studies showed that demographic characteristics (e.g., age, gender, educational background in high school, & employment status), classroom characteristics (e.g., course difficulty), cognitive engagement (e.g., exercise, seeking help, studying on weekends), and behavioral engagement (e.g., interaction in the online tutorial) are contributing factors for students being drop out (Ratnaningsih, 2011;Saefuddin & Ratnanningsih, 2008;Park & Choi, 2009;Sembiring, 2014;Sembiring, 2015;Coussement, Phan, De Caigny, Benoit, & Raes, 2020 The high number of non-active student at UT need to be solved. ...
Dropout rates in university that uses distance learning methods are definitely higher than those in conventional universities, including at Universitas Terbuka (UT) Indonesia. The term "drop out" is called non-active student in UT. This research aims to investigating the best time to identify students who become non-active and the student characteristics that have a higher risk of being non-active in distance learning. The data used in this study was provided by UT's Academic Information System Database (secondary data). Email surveys collected additional data. Logistic regression analysis was performed to identify students that are likely to drop out by Sociodemographic characteristics and their academic performance of students. This study reveals that grade point average (GPA) is an excellent predictor to identify students becoming non-active, especially in the first semester. We need to monitor student GPA throughout the first semester to prevent non-completion of their study, and it will improve the prediction accuracy.
... However, course-related factors are associated with learners' expectations and perceptions. Learners may feel unattended (Hart, 2012;Hone & el Said, 2016), impersonal supported (Henry, 2018), overwhelmed by perceived course difficulty (Greenland & Moore, 2022;Xenos et al., 2002) or without time management skills (Veletsianos et al., 2021). Those factors can be influenced by improving learner-related factors. ...
Dropout is one of the major problems online higher education faces. Early identification of the dropout risk level and an intervention mechanism to revert the potential risk have been proved as the key answers to solving the challenge. Predictive modeling has been extensively studied on course dropout. However, intervention practices are scarce, sometimes mixed with mechanisms focused on course failure, and commonly focused on limited interventions driven mainly by teachers' experience. This work contributes with a novel approach for identifying course dropout based on a dynamic time interval and intervening, focusing on avoiding dropout at the assessable activity level. Moreover, the system can recommend the best interval for a course and assessable activity based on artificial intelligence techniques to help teachers in this challenging task. The system has been tested on a fully online first-year course with 581 participants from 957 enrolled learners of different degrees from the Faculty of Economics and Business at the Universitat Oberta de Catalunya. Results confirm that interventions aimed at goal setting on the ongoing assessable activity significantly reduce dropout issues and increase engagement within the course. Additionally, the work explores the differences between identification mechanisms for course dropout and failure aiming to distinguish them as different problems that learners may face.
... program 1 (Heublein et al., 2020). Reasons for dropout can be manifold and include, for example, performance and financial problems (Heublein et al., 2017), as well as family-or health-related reasons (Xenos et al., 2002). In addition, motivational factors also play a major role in the decision to leave higher education (e.g., Heublein et al., 2017). ...
Autonomous motivation for self-set goals (pursuing goals for more intrinsic and less extrinsic reasons) has been linked to affective well-being. Using intensive longitudinal data, the present paper examines the link between university students' autonomous study motivation with affective well-being and targets fulfillment of the basic psychological needs as a potential mediating factor of this association on the within-person and the between-person level. University students in Germany (N = 488) completed an online questionnaire once a week over up to 2 semesters, indicating their weekly study motivation, need fulfillment, and affective well-being. Multilevel structural equation models were employed to target (a) the structure of autonomous study motivation and (b) the associations of autonomous study motivation with need fulfillment and affective well-being. Autonomous study motivation was associated with students’ positive and negative affect on both levels. In line with predictions by self-determination theory, multilevel mediation models suggested indirect effects via need fulfillment on both levels. Results highlight the central role of autonomous study motivation and need fulfillment in university students' socio-emotional adjustment. A better understanding of this socio-emotional adjustment of university students may be an important step for increasing overall study satisfaction and developing interventions to reduce study dropout.
... Some dimensions are easily addressed by the institution through institutional support, frameworks and best practices in course design and instruction (Lou et al., 2006). Other elements are challenging to address, including previous degrees and professional experience (Cochran et al., 2014;Dupin-Bryant, 2004;Levy, 2007;Xenos et al., 2002), prior online course experience (Dupin-Bryant, 2004), GPA (Cochran et al., 2014;Harrell and Bower, 2011;Jaggars et al., 2013b;McKinney et al., 2018), external support (Hart, 2012;Park and Choi, 2009), learning style (Harrell and Bower, 2011) and locus of control (Lee et al., 2012). Moderating variables for persistence in online Science, Technology, Engineering, and Mathematics (STEM) courses include demographic variables (e.g. ...
Purpose
As online course enrollments increase, it is important to understand how common course features influence students' behaviors and performance. Asynchronous online courses often include a discussion forum to promote community through interaction between students and instructors. Students interact both socially and cognitively; instructors' engagement often demonstrates social or teaching presence. Students' engagement in the discussions introduces both intrinsic and extraneous cognitive load. The purpose of this study is to validate an instrument for measuring cognitive load in asynchronous online discussions.
Design/methodology/approach
This study presents the validation of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions in an introductory physics course.
Findings
The instrument demonstrated reliability for a model with four subscales for all five discrete tasks. This study is foundational for future work that aims at testing the efficacy of interventions, and reducing extraneous cognitive load in asynchronous online discussions.
Research limitations/implications
Nonresponse error due to the unincentivized, voluntary nature of the survey introduces a sample-related limitation.
Practical implications
This study provides a strong foundation for future research focused on testing the effects of interventions aimed at reducing extraneous cognitive load in asynchronous online discussions.
Originality/value
This is a novel application of the NASA-TLX instrument for measuring cognitive load in asynchronous online discussions.
... A szakirodalom alapján láthatjuk, hogy a STEM képzések kiemelt szerephez jutottak az expanzió utáni felsőoktatásban. Ez részben az ide sorolható diszciplínákra jellemző, már említett magas lemorzsolódási arányok következménye (OECD 2019, Belloc et al. 2011, Duque 2014, Kori et al. 2015, Xenos et al. 2002, Ódor és Huszárik 2020 Rekrutáció és lemorzsolódás A nem-tradicionális hallgatók tömegeinek expanziót követő megjelenése nem független a lemorzsolódás fokozatos növekedésétől. Ezt támasztja alá Rumberger (2012) is, aki 400 szakirodalom alapján a lemorzsolódás rizikóját növelő faktorok két csoportját, az egyéni és intézményi tényezőket különítette el. ...
Bár a STEM (természettudományos, technológiai, mérnöki, matematikai képzésben részvevő) hallgatók lemorzsolódását, munkaerő-piaci kilátásait és a szakok elférfiasodását számos hazai és nemzetközi kutatás vizsgálta már, a STEM képzésekre felvételt nyert hallgatók szocio-demográfiai jellemzőivel és akadémiai felkészültségével kapcsolatban nemzetközi szinten is kevés szakirodalom áll rendelkezésre. Kutatásunk során az informatikai, műszaki és természettudományos szakok hallgatóit hasonlítottuk össze a nem-STEM szakos hallgatókkal az állandó lakhely településtípusa, a középiskolai osztályuk típusa, a hátrányos helyzetűek aránya, valamint a tanulmányi teljesítményért kapott többletpontok és felvételi összpontszám tekintetében. A STEM szakra való bekerülést magyarázó változókat bináris logisztikus regresszióval vizsgáltuk. Kutatásunk során 2017-es felvételi adatbázisból dolgoztunk, s kizárólag a nappali tagozatos munkarendű alap- és osztatlan képzésre bekerült hallgatók adatait elemeztük (N = 41324 fő). Eredményeink szerint a STEM hallgatók sem a szocio-demográfiai háttér, sem az akadémiai felkészültség tekintetében nem tekinthetők egyértelműen hátrányos helyzetű csoportnak a nem-STEM szakra felvettekhez képest. A létrehozott regressziós modell alapján elmondható, hogy a STEM képzésre való bejutás legjelentősebb prediktorai a nem (férfi), valamint a nyelvvizsgával és OKJ végzettséggel való rendelkezés. Kutatásunk fő kérdései arra vonatkoztak, hogy indokolhatja-e a STEM hallgatók alacsony státusa és hiányos akadémiai felkészültsége az ezeken a területeken megfigyelt kimagasló lemorzsolódási arányokat. Eredményeink alapján azonban ez nem jelenthető ki, így feltételezhetjük, hogy elsősorban intézményi tényezők (hűvös intézményi klíma, szelektív oktatói szemlélet, magas elvárások) állhatnak a lemorzsolódás mögött.
... Some empirical results suggest that the role of institutional factors in STEM courses may be more significant than in other courses. At the same time, curriculum difficulties, teaching standards, lack of student knowledge and self-confidence, and lack of counselling with educators in these areas can be identified as more common predictors of dropout (Marra et al., 2013;Xenos et al., 2002;Bocsi et al., 2019;Pusztai, 2019). ...
The high dropout ratios that characterize STEM fields can be explained by several factors, one of which is the employment of students during their studies. Our research aims to explore the impact of student employment on persistence and academic performance in STEM fields. During our research, we used the data of the Hungarian subsample of the PERSIST 2019 database (N=1045). According to our results, IT trainings are more resistant to the negative effects of employment than other trainings, as students working in this field did not differ from their non-employed peers in terms of either persistence or academic progress. Concerning the science, engineering, and non-STEM training, employment had a significant negative effect on academic performance, but it was primarily compensated by being employed in study-related jobs in science training, which also had a favorable effect in non-STEM training courses. However, in the field of engineering, employment in the profession reinforced this adverse effect too. In the field of informatics, study-related work also had a negative impact. Based on our results, we think it is worth considering the broader spread of dual training courses in higher education, during which students can complete an internship at a company or partner organization in addition to theoretical training.
... Various factors have been investigated, such as demographics (Xenos et al., 2002), students' behaviour and social positioning in forums (Murray, 2014) as well as educational background and peer influence (Yang et al., 2014). These factors can help researchers to better understand the reasons behind the success/failure or retention/dropout of the learners. ...
... Older studies in distance learning show dropout rates as high as 30% when online formats were introduced in college in the early 1990s (Ji-Hye & Hee, 2009). Reasons for higher dropout rates can be linked to the college student's previous academic performances in traditional coursework (Xenos et al., 2002). Some of the reasons adult students struggle with completing online work include not feeling they were placed in the appropriate course, finding it difficult to connect with classmates, the lack of one-to-one instruction, the difficulty of the material, and the lack of time to complete courses (Willging & Johnson, 2009). ...
In this qualitative study, adult students placed in an eight-week, closed-cohort GED preparation program were introduced to blended learning while attending in-person instruction five days a week during the COVID-19 pandemic. Participants had the opportunity to receive their curriculum in a blended format, which provided some convenience for the students. Unlike in most adult education programs, which are open entry in format, these students worked together to take the GED at the same time as a cohort. This study explored the students’ feelings about blended learning, their life challenges, past educational experiences and how the cohort model impacted their ability to complete the GED in a timely manner. Eleven students earned their GED and all but one student passed at least one section of GED by the end of the course. Further, only one student withdrew from the class. Eighteen students participated in individual interviews and a focus group to discuss their experience in the class. Students in the study attributed their success to a highly structured class, live instruction, and positive teacher and peer connections. Findings from this study also helped to restructure the program’s orientation processes, classroom instruction, and career services to better serve the adult education students of the Adult Learning Center of Osceola County, of Kissimmee, FL.
... According to Kranzow (2013), they range from 30% to 50%. In Europe, persistence rates vary from 70% to 80% while in Asian countries, these rates may be as high as 50% (Xenos et al., 2002). It should nonetheless be noted that there is a problem with reporting exact rates, as the definition of persistence varies from one author, institution, and country to another. ...
The aim of this study was to verify if external factors influence persistence in online courses in higher education. These external factors, borrowed from Kember’s (1995) model, included some students’ characteristics; cost benefits; social integration of adult students (enrolment encouragement, study encouragement, and family support); and external attribution (insufficient time, events hindering study, and distractions). Data were collected among a sample of 835 students from two Canadian French-Speaking Universities (n1 = 468 from University One and n2 = 367 from University Two) using an online questionnaire. The questionnaire included items borrowed from The Distance Education Student Progress (DESP) inventory (Kember et al., 1992). The multiple linear hierarchical regression analysis revealed that students’ characteristics and some of the external factors had an effect on students’ persistence in online courses and that the most important factor in predicting students’ persistence is cost benefits. These analyses were also conducted by university, gender, and age groups. Except for cost benefits, the results indicated different patterns of strength and significant relationships between groups.
... Also, [14] conducted a study that seeks to understand the high dropout rates, especially in science, and link them to the lack of basic skills in students entering university and [15] focused on the education of distance learning and aims to investigate the main causes for student dropouts. ...
... This finding implies that an increase in the age of a youth by a year will translate into a reduction of attrition in a training programme by about 0.003 per cent. This corroborates with Xenos et al. (2002) who opined that relatively older people are more likely to be retained in agricultural training programme compared to their younger counterparts. Our finding however, diverges from Mulholland et al. (2008) who found that the probability of older people to attrite from a training programme is high compared to younger ones. ...
Demographic data from Ghana suggest aging population among cocoa farmers. Based on this evidence, youth participation in the cocoa sector has become a subject of interest to stakeholders including Solidaridad. This study employed both qualitative analysis and parametric regression models that addresses observed and unobserved errors, to estimate the determinants of youth attrition from cocoa training programmes; and the possible impact of attrition on youth engagement in cocoa farming in Ghana. The empirical results reveal that younger and male youth, the unmarried, persons without mobile phone, youth who do not participate in community development initiatives, and youth without leadership roles have a higher propensity of attrition from agricultural training programmes. Attrition was also found to be significantly driven by youth not having savings, non‐business ownership, and having higher formal education. There is therefore the need for various stakeholders including COCOBOD in Ghana, to employ various mechanisms aimed at encouraging the youth to save. This could be done especially through the formation of Youth Savings and Loan Associations (YSLAs) which could reduce attrition rates from training programmes. Moreover, married and female youth should be prioritised by future cocoa‐training programmes, and encouraged to take up leadership roles in the communitiessince they have a lower probability of attrition from training programmes.
Identifying the positive attributes of students and instructors in the online environment will contribute to the understanding of how we can enhance the learning experience for the student and the teaching experience for the instructor. This article will assist students and instructors in understanding the differences that may be experienced in the online environment versus the face-to-face environment and provide the opportunity to consider whether online learning and/or teaching is a “good fit” for them. Understanding why students and/or instructors might choose the online environment will also assist administrators in developing successful, quality online programs that enrich the experiences for both students and instructors.
Este trabalho teve como objetivo principal identificar os principais fatores relacionados à evasão naeducação a distância em cursos de graduação em uma Instituição de Ensino Superior (IES) privadado Recife/PE. A metodologia utilizada consistiu na aplicação de um questionário estruturado a372 alunos que abandonaram o curso antes do término e também na utilização da análise fatorialexploratória via SPSS para tratamento dos resultados. Assim, com um Alpha de Cronbach de 0,966para o teste de confiabilidade do questionário, os resultados revelaram que os cursos com o maiornúmero de evadidos foram os de administração de empresas, logística, gestão comercial, marketinge processos gerenciais. Os principais motivos para a evasão foram: a dificuldade de acesso aoconteúdo, o desconhecimento sobre o funcionamento da modalidade, problemas financeiros edificuldades diversas de ordem pessoal, profissional e acadêmica que acabaram inviabilizando acontinuidade do curso. Palavras-chave: Educação a Distância; Evasão em EaD; Cursos de Gestão.
Anadolu Üniversitesi Açıköğretim Sisteminde (AÖS) ortak ders olarak birden fazla programda yer alan dersler mevcuttur. Yabancı Dil, Türk Dili vb. olan bu dersler, temel olarak bir programa ait olup başka programlarda da okutulmaktadır. Bu derslerin hangi program bünyesinde yürütülmesi gerektiği ile ilgili 2021-2022 öğretim yılından itibaren belirli kriterlere göre bir programa atanmasına yönelik aidiyet çalışması gerçekleştirilmiştir. Araştırmada, programların ders aidiyetlerine göre incelenmesi ve bu aidiyetlerin öğrenenlere kayıt öncesi destek vermesi amaçlanmış olup bu amaç doğrultusunda öğrenenler için bir kayıt destek sisteminin geliştirilmesi planlanmıştır. Kayıt destek sisteminde bir programın ders ağırlığının nasıl olduğu, öğrenenin ilgisini çeken derslerin hangi programlarda okutulduğu gibi bilgilerin sunulmasına yönelik çalışma gerçekleştirilmiştir. Araştırma, nitel araştırma desenlerinden durum çalışması ile desenlenmiş ve veriler, doküman analizi kullanılarak incelenmiştir. Araştırmanın verileri, Anadolu Üniversitesi AÖS’te ders aidiyeti uygulamasının yapıldığı 65 programdaki dersler ve bu derslerin aidiyetleri incelenerek elde edilmiştir. Elde edilen bulgulara göre toplam 1.133 dersten 252’si birden fazla programda okutulurken 881 ders ise sadece bir programda okutulmaktadır. 43 ön lisans programında ders listesindeki derslerin aidiyet benzerlik oranı %48,64 iken lisans programlarında bu oran %56,25’tir. 65 programa göre bu oran %52,17’dir. Yaklaşık olarak her iki dersten birinin ders aidiyetinin başka programda olduğu ve seçmeli ders uygulaması bulunmadığı göz önünde bulundurulduğunda sunulacak kayıt destek sisteminin öğrenenlerin kayıt süreçlerini önemli ölçüde kolaylaştıracağı öngörülmektedir.
Students learn not only directly from their teachers and books, but also by using their computers, tablets and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this work is based on gathering real time events as students interact with learning tools and materials in electronic devices, both in and out of class. Our study shows that the analysis of these events can provide teachers with week-by-week predictions of their students' final grades and help them to identify at an early stage those students at risk of failing. A blended environment university course in which students are expected to work autonomously out of class, but also attend face-to-face lessons was used as case study. Results show that predictions are reasonably accurate even during the first weeks of the course.
A substantial number of educational problems are complex and there are no quick solutions with immediate answers to solve them. Evasion is one of these problems and deserves special attention, which justifies the need for studies, research and critical reflections upon nuances that underlie, permeate and constitute approaches that can contribute to understanding and fighting this phenomenon. In distance learning, this phenomenon is even more prevalent, as evasion can be influenced by the use of educational platforms, according to more recent studies. The resources of educational technologies that distance learning uses allow the construction of knowledge to take place in different spaces, with teachers and students developing activities in different places or times. This study, based on a current theoretical framework, analysed the educational resources available in Virtual Learning Environments and their impact on evasion in distance learning Higher Education courses. Based on a qualitative approach, the case study was conducted using data from a Brazilian public university that identified the most used educational technology resources and related them to dropout rates. The results obtained suggest that the diversity of resources can impact those rates. The data analysis comprised eight courses, five academic semesters, 257 virtual classrooms, 19 educational resources and 1.023 students. Thus, the use of indicators in the creation of distance learning undergraduate courses is recommended to reduce dropout rates.
Dropout is a global and complex phenomenon affecting all universities. This study aimed at investigating the intrinsic and extrinsic factors causing dropout at the Open University of Mauritius (OU). Secondary data about 1885 learners were collected for five intakes and three levels of study - foundation, undergraduate and Master courses. The dropout rates were 46.08%, 39.14% and 17.31% respectively, showing that those with previous tertiary education were less likely to drop out of studies. Analysis of data from 96 completed questionnaires revealed that female learners were more persistent in their studies while mature students were less likely to drop out from university. Personal and career- related issues were the major causes leading to attrition at OU. Sub causes included wrong choice in programmes, inadequate tutorial support and lack of employer’s support. Corrective actions suggested include providing counselling sessions before registration and during studies, implementing strategies to help students develop time management skills, developing courses in line with industry requirements and improving tutor’s support. The low attrition rate at OU is probably because most of its learners are working adults, able to shoulder learning with greater responsibility and because it has maximised on technology to reach out to learners, hence mitigating isolation.
El fenómeno de la deserción en la enseñanza superior viene siendo investigado en diferentes escenarios, abordajes metodológicos y cortes temporales. El alcance de esta investigación se encuentra en entender por qué el joven estudiante de una institución de enseñanza superior (IES) confesional perde su interés en cursar administración. A través de una investigación cualitativa, por el método del estudio de caso, esta investigación objetivó levantar los motivos que llevan a los jóvenes de una institución de enseñanza superior confesional a abandonar el curso de Administración antes de su final. Los resultados han evidenciado que la deserción del joven investigado puede originarse de una acumulación de factores que generan insatisfacción y frustración con la experiencia estudiantil, como cuestiones pedagógicas, administrativas y personales. Además, es necesario señalar que los profesores necesitan comprender la necesidad de resignificar el aprendizaje para estos nuevos discentes. Dado este panorama, parece ser razonable asumir que este trabajo puede ayudar a comprender el universo juvenil y sus insatisfacciones, sobre todo en el plano de la enseñanza superior. Aparte de estos aspectos, estudios futuros en otros cursos, otras instituciones y también en otras regiones del país, podrán auxiliar en la construcción de un panorama más completo sobre el tema.
In recent years, the application of digital technologies for learning purposes is increasingly discussed as smartphones have become an integral part of students’ everyday life. These technologies are particularly promising in the so-called “transition-in” phase of the student lifecycle when first-year students start to develop a student identity and integrate into the university environment. At that stage, most premature dropouts are observed, presumably due to a lack of self-organization or self-responsibility. Considering this, a mobile app to tackle insufficient student experiences, support learning strategies, and foster self-organization in the “transition-in” phase was developed. The research at hand proposes a generalizable success model for mobile apps with a focus on first-year students, which is based on the IS success model (Delone and McLean in Inf Syst Res 3(1):60–95, 1992) and analyzes those factors that influence student satisfaction with such an app, the intention to reuse the app, and—foremost—students’ learning effectiveness. The results indicate that learning effectiveness is determined both by the perceived user satisfaction and users’ intention to reuse, which are particularly influenced by perceived enjoyment but also system and information quality. Finally, design principles are derived to develop similar mobile solutions.
University dropout is a concern for educational institutions since it directly impacts management and academic results, as well as being directly related to social problems. The literature points out that analyzing this phenomenon is a positive factor for developing programs to combat dropout, in addition to planning interventional actions and academic monitoring, making it possible to identify students at risk of dropout through techniques that use Machine Learning, for example. This paper presents a panoramic study of a public university, in which the school data were analyzed and classified using Machine Learning. The analysis of the data allowed to obtain an overview of the dropout data of the studied university. In addition, the main stakeholders were interviewed to report their main difficulties to know statistics about dropout. Considering these different data sources, we created digital reports to professors, chiefs and academic assistants, with information and statistics to assist university managers in decision-making related. These reports were validated by stakeholders and we hope that the next decisions can minimize any problems related to mental health, thus improving the quality of life of students, as well as their academic trajectory.
Massive Open Online Courses (MOOCs) have democratized access to higher education, but low entry and exit barriers make dropout rates in these courses much higher than in traditional in-person courses. Previous research has explored the main factors driving student dropout and proposed predictive models to identify students at risk. However, it is still unclear what type of interventions should be implemented to effectively reduce the dropout rate in MOOCs. We use findings from the literature to propose a theoretical framework to guide the design of interventions to reduce dropout in MOOCs. We design four interventions and implement them using A/B testing and natural experiment approaches in courses of the MITx MicroMasters® Program in Supply Chain Management (a MOOC-based program). Our findings reveal that ad hoc interventions communicated via email are not effective in reducing dropout, but interventions that modify course contents to make them more approachable for students show a positive impact on dropout reduction. By proposing a theoretical framework and uncovering the design features that can help to create effective interventions for dropout reduction, we provide a foundation for further research in the area of dropout interventions. This paper offers guidance to MOOC designers and instructors on how to improve student engagement and increase completion rates.
Computers and information technology play an important role in engineering education at the School of Engineering and Technology, Deakin University, Australia. Experience has shown that there are significant demographic differences between on- and off-campus engineering student groups. It was thought that the differences in the student groups might also lead to differences in computer usage patterns between the groups. A survey on computer usage was undertaken to determine the computer usage patterns of students at the commencement of their studies. The survey revealed differences between on- and off-campus students in whether students indicated they were regular computer users (on-campus = 77.2%, off-campus = 94.7%), the average reported hours per week usage of computers (on-campus = 6.0 h, off-campus = 23.3 h), the reported source of computer access, whether students indicated they were regular users of e-mail (on-campus = 29.8%, off-campus = 73.7%), whether students indicated they were regular users of the World Wide Web (on-campus = 38.6%, off-campus = 68.4%), and the reported source of World Wide Web access. It is proposed that the differing personal circumstances of the two student groups may contribute to the difference in survey responses.
The effects of reward or reinforcement on preceding behavior depend in part on whether the person perceives the reward as contingent on his own behavior or independent of it. Acquisition and performance differ in situations perceived as determined by skill versus chance. Persons may also differ in generalized expectancies for internal versus external control of reinforcement. This report summarizes several experiments which define group differences in behavior when Ss perceive reinforcement as contingent on their behavior versus chance or experimenter control. The report also describes the development of tests of individual differences in a generalized belief in internal-external control and provides reliability, discriminant validity and normative data for 1 test, along with a description of the results of several studies of construct validity.
A study of student attributes and achievements in previous Open University courses was undertaken to find out what affect they had on the drop‐out rate and achievements on a project work course. A special case of multidimensional analysis called partial order scalogram analysis has been employed in this research.The main aim of the study was to help the staff when advising prospective students (of various drop‐out risks) and to distinguish between students requiring different levels of assistance on the course.The findings, based on a sample of 445 students from 1982–;88, indicate that particular attributes such as occupation affect the drop‐out rate, while achievement in the project course appears to be influenced by the students’ performance in some previous courses.
This study examines which factors make a difference in distance learners' achievements and also their continuous enrolment status over one year in Korea National Open University. A path analysis revealed that while learners' job lo ad had no directly significant effect on grade-point average (GPA), it had a significant i ndirect effect on GPA via the study time variable. In addition to study time, social integration and extra face-to-fa ce activities were found to be significant variables ac ting on their GPA. In predicting subsequent enrolment, face-to-face activities were the most influential up to a year later. The results of this study challenges the prevalent view that GPA has something to do with drop-out, suggesting that learner progress toward achievement and drop-out may be understood better when viewed separately, especially in the case of long-term distance learning.
In 1990, a study was conducted at Snead State Junior College (SSJC) of students who were enrolled at the college in fall 1989 or winter 1990 but did not return in the subsequent quarter. Of 661 students identified as nonreturning, 489 were surveyed by telephone to determine their reasons for not returning. Demographic data on these students were obtained from college records to generate a profile of nonreturning students and to compare demographic data with stated reasons for not returning. Study results included the following: (1) the nonreturning student profile did not differ greatly from the student body as a whole, although slightly more female and day students did not return; (2) younger students and male students dropped out more often because of conflicts between school and jobs, while female students more often cited family responsibilities; (3) 32% of the respondents indicated an intention to return to SSJC at a later date; and (4) except for difficulties with scheduling necessary classes, most of the reasons given by students for not persisting were related to factors external to and beyond the control of the college. Based on study findings, it was recommended that the schedule of classes be planned on a yearly basis and made available to counselors and students; that the college develop a plan for continuing contact with nonreturning students and develop a better system for long-range enrollment analysis; and that the study of attrition be repeated in 3 to 5 years. A literature review and the survey instrument are included. (JMC)
Of 115 adult literacy students interviewed, 59 were contacted 8 months later to collect data on characteristics, academic and social integration, program and goal commitment, and withdrawal decisions. Factor and discriminant analyses identified 14 variables that accurately predicted the present status of 76 percent of completers, persisters, and dropouts. (SK)
This review of the literature examined a number of specific factors as they relate to the persistence and achievement of persons involved in distance learning. These factors are: (1) media of instruction; (2) instructor contact; (3) locus of control; and (4) certain demographic characteristics such as age, gender, income, and educational level. In preparation for the review, a search of a number of databases, including the Educational Resources Information Center (ERIC), Dissertation Abstracts Online (DAO), and Psychological Abstracts, was undertaken using these descriptors: attrition, dropout or persistence in distance education, achievement and performance in distance education, locus of control, instructor support and delivery model and demographic factors impacting distance education. It is clear from the review that the findings regarding the impact of the various factors on educational outcome are mixed due to inconclusive research findings and the lack of generalizability, a finding attributed to the lack of common definitions. (Contains 92 references.) (Author/SLD)
This paper describes a study by the American Council on Rural Special Education (ACRES) to determine its membership's opinions on priorities and expertise regarding the dropout problem. ACRES members, primarily rural special-education teachers, parents, and collateral service workers, were surveyed about the dropout problem and what research they considered necessary for an effective intervention program. Four sets of variables affecting students' decisions to drop out of school are specified, based on a literature review. They are: family, school, peer groups, and intrapersonal influences. These four variables were used to generate a scale of dropout causes used in the study. A total of 305 ACRES members responded to the mail survey in fall 1989. Respondents rated causes (e.g.,"frustrations") in terms of national importance and as to whether or not enough research had been done. Factor analysis and Chi-square analysis of the data provide no clear direction as to which needs should be addressed immediately. Survey responses were further correlated with years of respondents' teaching experience, but no significant links were found. The authors advise readers not to accept any of the alleged causes of dropping and not to expand existing programs without first: (1) establishing sufficient research evidence that the proposed treatment will be effective; or (2) developing public awareness that the existing research is substantial. (TES)
The Instructional and Performance Technology Department at Boise State University (Idaho) offers a master's degree program via distance education to prepare adult students for careers in the areas of instructional design, job performance improvement, human resources, organizational redesign, training, and training management. Most students attend the program not only to earn a master's degree, but also to upgrade professional knowledge and skills. This paper discusses the problem of adult student dropouts, how a solution was approached, and results obtained. From the cause analysis, it was concluded that satisfaction during the first or second courses was the major factor that determined students' decisions whether or not to continue in the program. Forty-two percent of the students who dropped out expressed dissatisfaction with the learning environment as the reason; another reason was a discrepancy between professional or personal interests and course structure. The instructor who taught the introductory course systematically redesigned the curriculum and developed strategies to improve students' attention toward learning, make the learning more relevant to their professions, increase confidence levels, and increase satisfaction with both the learning subject and the learning environment. Since the new interventions were implemented, significant improvement in student retention has been achieved. (AEF)
In this paper we present an innovative framework for the teaching of computer literacy and application that can serve as a new educational paradigm in teaching courses in a distance learning format. This new framework combines and integrates new technologies with older distance education teaching aids. We implement this framework in a course called “Computer Applications for Social Sciences” that was developed in the Open University of Israel, a recognized academic institution in Israel. The course, which is based on distance learning and electronic tools such as communication technologies, CD-ROM coursewares, Web-sites and discussion groups, was taught in the second semester of 1999. The course was developed in a way that allows students with no previous knowledge to learn it at a distance. We present here a description of the course and its e-learning tools, a broad study on the 219 students who participated in the course, and a close study on 55 of these students.
Thesis (Ph. D.)--Graduate School, Pennsylvania State University. College of Education. Includes bibliographical references (p. 168-178).
ACRES at-risk task force: dropout survey (pp. 18–23) Annual Conference of the American Council for Rural Special Education A multivariate model for evaluating distance higher education
- K S Bull
Bull, K. S. (1990). ACRES at-risk task force: dropout survey (pp. 18–23). Annual Conference of the American Council for Rural Special Education, Tuscon. Chacon-Duque, F. J. (1987). A multivariate model for evaluating distance higher education. Pennsylvania State Uni-versity Press: College Park.
Distance learning, the Internet and the World Wide Web. ERIC Digests
- S Kerka
Kerka, S. (1996). Distance learning, the Internet and the World Wide Web. ERIC Digests, ERIC Document Repro-duction Service No. ED395214. Lupo, D., & Erlich, Z. (2001). Computer literacy and applications via distance e-learning. Computers & Education, 36(1), 333–345.
The discourse of dropout in distance education: a theoretical analysis Annual Conference of the Canadian Association for the Study of Adult Education Issues in oreparing Open University learners for Open University system
- J Munro
Munro, J. (1987). The discourse of dropout in distance education: a theoretical analysis. Annual Conference of the Canadian Association for the Study of Adult Education, Alberta, Canada. Narasimharao, B. (1999). Issues in oreparing Open University learners for Open University system. Available: http:// www.cemca.org/ignou-icde/paper23.html.
Technical and methodological principles
- A Anastasi
- S Urbina
Anastasi, A., & Urbina, S., (1997). Technical and methodological principles. In: Psychological testing, Ed. (7th) Prentice
Hall, New Jersey.
Adult distance education, educational technology and drop out. The New Zealand Technical Correspondence Institute's Management Courses
- R Ostman
- R Colle
Ostman, R., & Colle, R. (1988). Adult distance education, educational technology and drop out. The New Zealand Technical Correspondence Institute's Management Courses. Studies in Education, No. 48.
Measures of association and their tests of significance
- S Siegel
- N J Castellan
Siegel, S., Castellan, N. J. (1988). Measures of association and their tests of significance. In Nonparametric statistics for the behavioral sciences (2nd ed.). New York: McGraw-Hill Book Company Inc.
Assignements in Distance Education – An Overview, Epistolodidaktika
- J Baath
Baath, J., (1994). Assignements in Distance Education – An Overview, Epistolodidaktika, No. 1, pp. 13-20.
An exploration of learner progress and drop-out in
- N Shin
- J Kim
Shin, N., & Kim, J., (1999). An exploration of learner progress and drop-out in Korea National Open University, Distance
Education – An International Journal, Vol. 20, No 1.
Issues in Preparing Open University Learners for Open University System Available at the web site http
- B Narasimharao
Narasimharao, B., (1999). Issues in Preparing Open University Learners for Open University System. Available at the web
site http://www.cemca.org/ignou-icde/paper23.html.
Issues in oreparing Open University learners for Open University system
- B Narasimharao
Assignements in distance education—an overview
- Baath
The discourse of dropout in distance education: a theoretical analysis. Annual Conference of the Canadian Association for the Study of Adult Education
- J Munro