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Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes
... We identified three studies that used simulation-based environments and two studies that used intelligent tutors (ITS). Several other types of systems were used, such as a learning software (Siadaty et al., 2016a, b), an e-learning application (Goda et al., 2015) and a game (Ternblad et al., 2022). ...
... In these studies, dimensions derived from multiple SRL models are distilled into a single customized framework (Suraworachet et al., 2023;Segedy et al., 2015;Zheng et al., 2020;Suraworachet et al., 2023). In addition, several studies examined dimensions of SRL without specifying any model or framework (Goda et al., 2015;Pardo et al., 2017;Li et al., 2018). Finally, some studies do not fit into the precedent categories and did not specify the theoretical basis of SRL (Dang & Koedinger, 2020;Quick et al., 2020). ...
... These metrics emphasize process-oriented aspects of learning, such as learner effort (total number of attempts) (Molenaar et al., 2021), and learning pace (i.e. completion rate) (Goda et al., 2015). These metrics are typically used not only to assess learner's performance, but also to measure their engagement patterns and predict potential outcomes based on their diligence and interaction levels (You, 2016;Cicchinelli et al., 2018;Quick et al., 2020;Goda et al., 2015). ...
Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and 2023. The goal of this review is to exhibit current research orientations, characteristics of their contexts, and the SRL theories they rely on. A part of this study is then dedicated to a categorization of the trace data and indicators used to capture SRL behaviors as well as automatic methods to leverage them. Finally, we describe how learning scaffolds are provided to support SRL. In this respect, current research has brought methodological and theoretical insights to the study of SRL, particularly through the analysis of student’s behaviors and profiles, and their relationship to learning performance. Researchers guide their work by using recognized theoretical models of SRL to map trace data into SRL processes and dimensions. Future research is motivated by the development of learning platforms that go beyond data collection to incorporate learning analytics tools that provide personalized SRL support to students.
... A higher priority for achieving future goals is to expand research to understand the certainty of its existence and the magnitude of its impact and to identify the need for interventions to substitute for in-class monitoring of students' efforts. Therefore, this study is a modified replication of Goda et al. (2015) and Li et al. (2018) among the few previous studies. Goda et al. (2015) conducted a longitudinal analysis to observe university students' online EFL learning behavior. ...
... Therefore, this study is a modified replication of Goda et al. (2015) and Li et al. (2018) among the few previous studies. Goda et al. (2015) conducted a longitudinal analysis to observe university students' online EFL learning behavior. The authors, who examined the relationship between learning behavior types and learning outcomes, found that procrastinators had lower test scores than habitual learners. ...
... While studies by Goda et al. (2015) and Li et al. (2018) are valuable and informative, they use final-course grades or post-completion test scores as indices of learning outcomes. They do not consider pre-course proficiency or explore the relationship between procrastination and proficiency growth. ...
Although one advantage of asynchronous online language learning is that learners can choose when and where to learn, this learning environment can also lend itself to procrastination. However, procrastination during asynchronous language learning has not been sufficiently studied. Therefore, the present study investigates student procrastination in an asynchronous English learning environment and examines its impact on learning outcomes. University students engaged in asynchronous learning were divided into three groups, reflecting the timing of access to online materials: procrastinators, habitual learners, and uncategorized learners. When the pre- and post-test scores of the three groups were compared, the procrastinators showed significantly less score growth than the habitual learners. However, these results leave room for further research on the learning outcomes of uncategorized learners, who lacked good study habits but did not procrastinate. The results suggest that, even in asynchronous language learning environments characterized by anytime/anywhere learning, interventions are needed to help students avoid procrastination and become habitual learners.
... Por ello, el ritmo regular de aprendizaje o hábito de estudio es considerado un importante factor para predecir la permanencia de los estudiantes en los estudios virtuales (Lim, 2016). Un hábito de aprendizaje constante ha demostrado ser más efectivo para el aprendizaje en entornos virtuales, en la medida que se condice con mejores resultados, los aprendizajes y la permanencia de los estudiantes de los programas (Goda et al., 2015). ...
... Las habilidades iniciales para el desarrollo de aprendizaje online, que se relacionan tanto con aprendizajes anteriores como también la formación para la autogestión del aprendizaje, tienen influencia importante en el éxito de los estudios online (Choi & Kim, 2018;Goda et al., 2015;Heidrich et al., 2018;Lim, 2016). Para el caso estudiado, ambos contenidos son parte del curso que desarrolla la institución, ya que funciona como un nivelador en habilidades de lenguaje y prepara a los alumnos para conocer estrategias de autogestión y metacognición del aprendizaje. ...
... Es así como las acciones relacionadas con el acompañamiento de los tutores y la atención de la Unidad de Apoyo Socioafectivo, notoriamente, se vinculan a la contención necesaria que deben sostener a estudiantes adultos cuyo entorno es complejo y presentan dificultades que requieren ser orientadas y apoyadas desde alguna unidad o programa complementario (Choi & Kim, 2018;Kara et al., 2019). Mientras que la aprobación del curso de habilidades básicas y la participación en actividades de inducción repercuten, en forma directa, en apoyar algunas debilidades que ya se identifican como propias de la deserción en los sistemas formativos online (Heidrich et al., 2018), específicamente apoyan el desarrollo de habilidades para la autogestión y organización del tiempo de estudio, que muestran ser una dificultad permanente para los estudiantes en esta modalidad (Goda et al., 2015;Lim, 2016;Su & Waugh, 2018). A la vez, les proporciona información básica sobre cómo funciona la plataforma de aprendizaje, la comunicación con sus profesores y padres, y una serie de elementos que van a ser importantes dentro de todo el proceso formativo, especialmente para sentirse integrados como parte de la institución (Cacciamani et al., 2019;Rovai & Wighting, 2005). ...
During the last few years there has been an increase in online training programs, an opportunity for those who need to reconcile studies with other activities. Retention rates in this type of training are low. There are multiple factors that have been identified as part of the problem of continuity in studies for adult students in online programs. The method
design is mixed, of the sequential explanatory type. The quantitative phase is an ex post facto study of the causal comparative type that allowed to identify significant differences between groups for the variables under study, from a sample of 9,405 first-year students.
The qualitative phase included the application of interviews. It was found that the tutor's orientation, passing basic skill courses, the participation of the students in induction activities and having the attention of the socio-affective support unit, directly influence
academic persistence in students, as well as their, participation in online platforms and grade averages. Students value effective communication with teachers and tutors. It is from the diversity of actions undertaken that a containment network is generated to
support the process of continuity of studies. It is not possible to attribute success to a single action separately, and we deduce from in-depth interviews that, also, the influence of each program is different in relation to the needs presented by each student, showing the importance of institutions deploying various support actions, which will be used by students to the extent of their needs.
... Por ello, el ritmo regular de aprendizaje o hábito de estudio es considerado un importante factor para predecir la permanencia de los estudiantes en los estudios virtuales (Lim, 2016). Un hábito de aprendizaje constante ha demostrado ser más efectivo para el aprendizaje en entornos virtuales, en la medida que se condice con mejores resultados, los aprendizajes y la permanencia de los estudiantes de los programas (Goda et al., 2015). ...
... Las habilidades iniciales para el desarrollo de aprendizaje online, que se relacionan tanto con aprendizajes anteriores como también la formación para la autogestión del aprendizaje, tienen influencia importante en el éxito de los estudios online (Choi & Kim, 2018;Goda et al., 2015;Heidrich et al., 2018;Lim, 2016). Para el caso estudiado, ambos contenidos son parte del curso que desarrolla la institución, ya que funciona como un nivelador en habilidades de lenguaje y prepara a los alumnos para conocer estrategias de autogestión y metacognición del aprendizaje. ...
... Es así como las acciones relacionadas con el acompañamiento de los tutores y la atención de la Unidad de Apoyo Socioafectivo, notoriamente, se vinculan a la contención necesaria que deben sostener a estudiantes adultos cuyo entorno es complejo y presentan dificultades que requieren ser orientadas y apoyadas desde alguna unidad o programa complementario (Choi & Kim, 2018;Kara et al., 2019). Mientras que la aprobación del curso de habilidades básicas y la participación en actividades de inducción repercuten, en forma directa, en apoyar algunas debilidades que ya se identifican como propias de la deserción en los sistemas formativos online (Heidrich et al., 2018), específicamente apoyan el desarrollo de habilidades para la autogestión y organización del tiempo de estudio, que muestran ser una dificultad permanente para los estudiantes en esta modalidad (Goda et al., 2015;Lim, 2016;Su & Waugh, 2018). A la vez, les proporciona información básica sobre cómo funciona la plataforma de aprendizaje, la comunicación con sus profesores y padres, y una serie de elementos que van a ser importantes dentro de todo el proceso formativo, especialmente para sentirse integrados como parte de la institución (Cacciamani et al., 2019;Rovai & Wighting, 2005). ...
La oferta formativa de programas online ha aumentado, siendo una oportunidad para quienes requieren compatibilizar el estudio con otras actividades. Las tasas de retención en este tipo de formación son bajas. Múltiples factores explican la deserción de estudiantes adultos en programas online. El diseño es mixto, del tipo explicativo secuencial. La fase cuantitativa permitió identificar diferencias significativas entre grupos para las variables en estudio, a partir de una muestra de 9.405 estudiantes. La fase cualitativa contempló la aplicación de entrevistas. Se pudo constatar que el acompañamiento del tutor, la aprobación de los cursos de habilidades básicas, la participación de los estudiantes en actividades de inducción y contar con la atención de la unidad de apoyo socioafectivo, influyen directamente en la deserción de los estudiantes, en su participación en la plataforma online y en el promedio de calificaciones. Los estudiantes valoran la comunicación efectiva con docentes y tutores. Se debe generar una red de contención para apoyar el proceso de continuidad de estudios con diversas acciones, también, la influencia de cada programa es distinta en relación con las necesidades que presenta cada alumno, mostrando la importancia de que las instituciones desplieguen una diversidad de apoyos, los que serán utilizados por los estudiantes en la medida de sus necesidades.
... SR learners are those who can prepare a learning plan, adjust it, and apply self-control and self-evaluation (Deci, Ryan, and Williams, 1996). Goda, Yamada, Kato, Matsuda, Saito, and Miyagawa (2015) suggested that high-level SR learners can control and manage their learning plan in the context of their everyday lives. SRL is closely linked to the concept of autonomy, particularly in the aspects of metacognition, motivation, and learning behavior (Schunk and Zimmerman, 1998;Zimmerman, 1986), which enables learners themselves to take responsibility for learning. ...
... The results revealed that goodachievement learners tended to link the pages and knowledge with learning materials. Goda et al. (2015) identified seven distinct learning behavior types using learning logs, 1: procrastination, 2: learning habit, 3: random, 4: diminished drive, 5: early bird, 6: chevron, and 7: catch-up. They revealed that the students who had the learning habit type and chevron type gained better scores than the procrastination type. ...
This research aims to investigate the relationship between self-regulated learning awareness, learning behaviors, and learning performance in ubiquitous learning environments. In order to do so, psychometric data about self-regulated learning and log data such as marker, annotation, accessing device types that stored the learning management system were collected and analyzed using multiple regression analysis with stepwise method. The results indicated that self-efficacy, internal value, and the number of read slides had a significant influence on the final score, and the awareness of cognitive learning strategy use has slightly significant power to predict the final score.
... Procrastination was observed in traditional classes with face-to-face learning and in an online setting (Goda et al., 2014;Hong et al., 2021;Pelikan et al., 2021a). Levy and Ramim (Levy & Ramim, 2012) found that 58% of students procrastinate during online learning. ...
... Levy and Ramim (Levy & Ramim, 2012) found that 58% of students procrastinate during online learning. In line with that, Goda et al. (Goda et al., 2014) have reported that students in Japan procrastinate pretty high (69.16%) during online learning. ...
Procrastination is a prevalent phenomenon in academic contexts. During online learning, the problem of postponing may become more prone to occur. Previous studies have shown that self-regulated learning has a robust effect on procrastination. However, researchers rarely consider the link between patience, self-regulated learning, and procrastination. This study investigated the role of patience and self-regulated learning on academic procrastination. We propose a mediation model that self-regulation would mediate the relationship between patience and procrastination. A total of 290 undergraduate students from a private university in Yogyakarta, Indonesia, enrolled in this study. There were 103 males and 187 females aged 17-24 (M=19.5 years). We collected the data by administering patience, self-regulated learning, and academic procrastination questionnaires. The result showed that patience, self-regulated learning, and procrastination were correlated significantly. Bivariate correlation showed patience positively and significantly associated with all self-regulated learning subscales: goal-setting, environmental structuring, time-management, help-seeking, self-evaluation, and task strategies. Furthermore, mediation analysis gives evidence that patience indirectly affects procrastination via student self-regulated learning. Only goal setting and time management could mediate the relationship between patience and academic procrastination at the specific indirect level. These findings highlight the role of patience in strengthening self-regulation and explain the underpinning mechanism between patience and procrastination.
... The data was collected in September 2022 from social media platform X, formerly known as Twitter, using the standard application programming interface. The social media platform hosts over 300 million users worldwide (Fiegerman, 2017), and the data is publically accessible. A purposive sampling was used. ...
... Our findings tally with that of Reid et al. (2016), who noted that students were overwhelmed with the large workload. Goda et al. (2015) attributed missed assignment deadlines to students' poor resource management skills and insufficient time. However, it would be important to separate the two. ...
***** Purpose
E-learning has become a polarizing issue. Some say that it enhances accessibility to education and some say that it hinders it. While the literature on the subject underscores the effectiveness of the pedagogical frameworks, strategies and distance learning technologies, the firsthand accounts of students, parents and practitioners challenge the validity of experts’ assessments. There is a gap between theory and practice and between the perceptions of providers and consumers of online learning. Following a period of lockdowns and a transition to online learning during the recent pandemic, the prevailing sentiment toward a distance mode of instruction became one of strong skepticism and negative bias. The aim of the study was to examine why e-learning has struggled to meet stakeholder expectations. Specifically, the study posed two research questions: 1. What are the reasons for dissatisfaction with online learning? 2. What are the implications for future research and practice?
***** Design/methodology/approach
The study used a mixed methods approach to examine the reasons behind negative perceptions of online learning by comparing the firsthand accounts posted on social media with the literature. To this end, n = 62,874 social media comments of secondary and postsecondary students, as well as parents, teachings staff and working professionals, covering the span of over 14 years (2008–2022), were collected and analyzed.
***** Findings
The study identified 28 themes that explain the stakeholder’s discontent with the online learning process and highlighted the importance of user-centric design. The analysis revealed that the perceived ineffectiveness of distance education stems from the failure to identify and address stakeholders’ needs and, more particularly, from the incongruence of instructional strategies, blindness to the cost of decisions related to instructional design, technology selection and insufficient levels of support. The findings also highlight the importance of user-centric design.
***** Practical implications
To address dissatisfaction with e-learning, it is imperative to remove barriers to learning and ensure alignment between technology and learners’ needs. In other words, the learning experience should be personalized to account for individual differences. Despite its cost-effectiveness, the one-size-fits-all approach hinders the learning process and experience and is likely to be met with resistance.
***** Originality/value
Drawing from the extensive literature, the study offers an explanation for stakeholders’ discontent with e-learning. Unlike survey research that is prone to social desirability bias, the sample provides a rare opportunity to observe and measure the visceral reactions that provide a more authentic sense of stakeholders’ perceptions toward online learning. The authors offer recommendations and identify areas for future research.
... Driver: The motivation for delaying feedback in this way is to help students develop good time-management skills. It is well known that many students put off working on large programming assignments until deadlines are imminent, and this procrastination can be a serious problem [5,9,12]. By scheduling the feedback opportunities, we incentivize students to design their own tests and to start their work early so that they can benefit from the feedback. ...
... A consequence of this, and in some cases the main driver, was to resolve issues associated with accuracy of marking.In some cases, AATs identify subtle errors that are difficult for a human to detect (Case 7). Also, with human markers, there is the concern that they are not always consistent in their interpretation of the marking rubric (e.g., Cases 8,12). ...
... Early online course abandonment is often due to the following widely identified factors: boredom, feelings of loneliness, the learning platform's technical complexity, lack of support for technical problems, lack of help with questions on certain concepts, lagging response times, difficulty in self-managing the pace of work and accumulation of work near due-dates (Bawa, 2016;Goda et al., 2015). Therefore, we designed the learning strategy focusing on maximizing student retention. ...
... The intended outcome of this mentoring strategy is to reduce the impact of any negative critical incidents on the students' perception, which has been pointed as a major factor in student dropout (Lin, 2011). Another benefit of close mentoring is to avoid the pernicious effect of procrastination, a notable factor that affects learning outcomes (Goda et al., 2015). ...
Motivation
E-learning is the standard solution adopted in transnational study programmes for which multiple face-to-face learning places are not an option. Bioinformatics is compatible with e-learning because its resource requirements are low. Online learning, however, is usually associated with high dropout rates because students start from a very low computational level and/or they need support to conduct practical analyses on their own.
Results
In this paper, we analyse the academic results of an online bioinformatics educational programme based on learning communities. The programme has been offered by the Spanish Pablo de Olavide University for more than five years with a completion rate of close to 90%. Learning bioinformatics requires technical and operational competencies that can only be acquired through a practical methodology. We have thus developed a student-centred and problem-based constructivist learning model; the model uses faculty and peer mentoring to drive individual work and retain students. Regarding our innovative learning model, the recruitment level (i.e. the number of applicants per available places and international origin), the results obtained (i.e. the retention index and learning outcomes), as well as the satisfaction index expressed by students and faculty lead us to regard this programme as a successful strategy for online graduate learning in bioinformatics.
Supplementary information
Supplementary data are available at Bioinformatics Advances online.
... Another rule showed that students who access the theoretical content early, who spend an average time on quizzes, and complete them in an average time show high success. Goda et al. (2015) analysed the behaviours of 441 students with learning materials in a learning management system. Learning paces were determined according to the completion rate of the learning materials. ...
This study aimed to develop a prediction model to classify students based on their academic procrastination tendencies, which were measured and classified as low and high using a self-report tool developed based on the students’ assignment submission behaviours logged in the learning management system’s database. The students’ temporal learning traces were used to extract the features used in the prediction models. The study participants were 51 students enrolled in the Database Management Systems course, which was conducted online using the Moodle learning management system. The study compared the performance of different machine learning algorithms in predicting students’ academic procrastination tendencies, analysed the important features of prediction models, and examined whether there is a difference between the academic performance of low and high academic procrastinators. Logistic regression was found to outperform other classification algorithms and reached 90% accuracy in classifying low and high academic procrastinators. Students’ regular and early access to course activities were found to be important features in predicting their academic procrastination tendencies. In terms of academic performance, the findings support the existing literature. Students with low academic procrastination tendencies got significantly higher final grades than those with high academic procrastination tendencies. These findings show that students’ academic procrastination tendencies can be predicted with high accuracy using online learning trajectories. Such a model will be important in the development of intervention methods for preventing academic procrastination.
... Procrastination has been identified as a significant correlate of academic performance and achievement (Joubert, 2015;Lindner et al., 2023). Given these implications for academic performance, addressing students' tendency to procrastinate is considered a top priority, particularly in online educational environments (Cerezo et al., 2017;Goda et al., 2015). Moreover, the psychological toll of procrastination, marked by increased stress and decreased self-efficacy, underscores the need for a nuanced understanding of this construct (Hailikari et al., 2021;Tice & Baumeister, 1997). ...
Procrastination in academic contexts is thought to have a negative effect on students’ learning and performance. This research sought to provide a comprehensive multimethod and multimodal validation of a self-report measure of procrastination, revealing its intricate associations with behavioral indicators of procrastination, engagement, and learning. A sample of 566 high school Advanced Placement Statistics students (Mage = 16.68 years) in the midwestern United States participated in the investigation. We used multiple methods and multiple traits to evaluate convergent and discriminant validity evidence. To gather evidence of convergent validity, we examined associations between self-reported procrastination and actual procrastination behavior extracted from log data using survival analysis. Students were much more likely to not submit assignments on time when self-reported academic procrastination was high. This trend persisted across all assignments, underscoring the robust link between self-reported procrastination and delayed completion. The results also highlighted strong significant negative correlations between self-reported academic procrastination and self-reported affective, behavioral, and cognitive engagement. To examine predictive validity, we also examined the association between self-reported academic procrastination and key learning outcomes. While self-reported procrastination was not significantly associated with students’ Advanced Placement exam scores, it was significantly and negatively associated with students’ final class grades. Students with greater self-reported procrastination tended to have lower class grades, consistent with prior work linking procrastination to suboptimal learning outcomes. These findings could inform educational interventions that reduce procrastination tendencies, enhance student engagement, and ultimately, academic success.
... Although interventions have traditionally been face-to-face, their practicality and costeffectiveness are limited by multiple factors such as availability, the skill of the professionals involved, access barriers to participation, and the time investment required for the intervention process (Rozental et al., 2015). So, in recent years there has been an increase in the number of online or hybrid interventions that could help reduce the barriers of face-to-face interventions (Cerezo et al., 2020;Goda et al., 2015). Web-based courses have tools that can benefit student learning, academic performance or even reduce procrastination, as they integrate synchronous and asynchronous communication systems and offer flexibility in the distribution of responsibilities between instructor and student (Tuckman, 2007;Tuckman & Schouwenburg, 2004). ...
Academic procrastination is a behaviour that delays the completion of tasks, time management or other academic activities. However, it is possible to develop interventions that eliminate or minimise this procrastinating behaviour. This study presents a systematic review of the literature following the PRISMA method guidelines, with the aim of identifying interventions for the reduction or elimination of procrastinating behaviours in university students. A search was conducted for records published in peer-reviewed scientific journals between 2010 and 2022. Three electronic databases were consulted. A total of ten studies that met the inclusion criteria were analysed. This review shows the main features of the included studies, with special emphasis on the changes perceived depending on the intervention modes applied: face-to-face, online and hybrid. The results revealed that face-to-face interventions showed high levels of changes perceived, as participants presented improved time management, task planning, awareness and reduced procrastination behaviour. However, although the hybrid and online interventions showed some changes in terms of the competencies identified, they did not report changes perceived in terms of changes in procrastination behaviour.
... To develop this survey, a literature review of learning and assessment strategies was conducted [22][23][24][25][26][27][28][29][30]. Approximately 50 different learning strategies were synthesized into the list shown in Table 1. ...
... On the other hand, providing learning opportunities and support according to the interests, learning styles, and cognitive characteristics of individual learners is called personalized learning [44] , and research findings that contribute to its realization have also been found. For example, Goda et al. [48] found that, in learning English on a computer, the scores of learners who accessed the learning support system in a planned and continuous manner and those who accessed the system ahead of the due date were significantly higher than those of learners who accessed the system just before the due date (learners with a procrastination tendency). Oyama et al. [49] showed that in foreign language learning, active learners tend to focus on areas that are indicated as "on the test," whereas reflective learners tend to follow the order of the material and return to those areas when they find mistakes, and that there are differences in the learning process depending on individual learning styles. ...
In this paper, we 1) review the trend of informatization in elementary and secondary education in Japan, 2) summarize the individualization of learning in today’s educational policy, and 3) attempt to map the outcomes of educational technology research to the present day. The trend of individualization of learning in elementary and secondary education in Japan is characterized by the following two points: (1) the appropriate treatment of learning content, represented by CAI (Computer Assisted Instruction), which has been studied in the field of educational technology for a long time, has been put to practical use; and (2) the results of recent research on self-regulated learning based on the achievements of the psychology of learning that are now being reflected in elementary and secondary education.
... As humans, we operate under the cadence of a seven-day week, and the week-day alternation and the time of day drive our lives. Since the flexibility of e-learning requires student self-regulation (time management and self-testing) (Goda et al., 2015), some questions arise. One of them is how students act at different times of the day. ...
Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable time-windows that could be used to investigate their temporal behaviour. First, we present a novel perspective for identifying different types of sessions based on individual needs. The majority of previous works address this issue by establishing an arbitrary session timeout threshold. In this paper, we propose an algorithm for determining the optimal threshold for a given session. Second, we use data-driven methods to support investigators in determining time-windows based on the identified sessions. To this end, we developed a visual tool that assists data scientists and researchers to determine the optimal settings for session identification and locating suitable time-windows.
... 4 In line with previous studies, this study's findings support the perspective that time management strategies have a considerable significant impact on academic achievement and, thus, should be considered as a variable worth addressing in future updates in the ALP and/or future intervention studies. 20,21 Given the scope of this study, we did not analyze detailed logs to objectively measure consistent study behaviors, via indicators such as frequency of accessed sessions, dates of first and last activities, and interval between activities, which is a promising area for future research, using sophisticated data mining techniques. ...
Objectives:
To examine the impact of dental students' usage patterns within an adaptive learning platform (ALP), using ALP-related indicators, on their final exam performance.
Methods:
Track usage data from the ALP, combined with demographic and academic data including age, gender, pre- and post-test scores, and cumulative grade point average (GPA) were retrospectively collected from 115 second-year dental students enrolled in a blended learning review course. Learning performance was measured by post-test scores. Data were analyzed using correlation coefficients and linear regression tests.
Results:
The ALP-related variables (without controlling for background demographics and academic data) accounted for 29.6% of student final exam performance (R2=0.296, F(10,104)=4.37, p=0.000). Positive significant ALP-related predictors of post-test scores were improvement after activities (β=0.507, t(104)=2.101, p=0.038), timely completed objectives (β=0.391, t(104)=2.418, p=0.017), and number of revisions (β=0.127, t(104)=3.240, p=0.002). Number of total activities, regardless of learning improvement, negatively predicted post-test scores (β= -0.088, t(104)=-4.447, p=0.000). The significant R2 change following the addition of gender, GPA, and pre-test score (R2=0.689, F(13, 101)=17.24, p=0.000), indicated that these predictors explained an additional 39% of the variance in student performance beyond that explained by ALP-related variables, which were no longer significant. Inclusion of cumulative GPA and pre-test scores showed to be the strongest and only predictors of post-test scores (β=18.708, t(101)=4.815, p=0.038) and (β=0.449, t(101)=6.513, p=0.038), respectively.
Conclusions:
Track ALP-related data can be valuable indicators of learning behavior. Careful and contextual analysis of ALP data can guide future studies to examine practical and scalable interventions.
... These include low self-efficacy, disorganization, low intrinsic motivation, poor effort regulation, and inadequate time management skills [75,76]. As a result, it is argued that academic procrastination could be a major contributor to poor academic performance and achievement [77][78][79][80][81]. ...
This article investigates the extent of academic procrastination among secondary school students in the Kingdom of Saudi Arabia who utilize sustainable digital learning materials, specifically e-books, compared to those who rely on printed books in a traditional face-to-face learning environment. A sample of 336 first-year secondary school students was randomly recruited and divided into two experimental groups based on their preference for either electronic or printed textbooks. An online survey was employed to assess academic procrastination. The findings indicate no statistically significant differences in the impact of textbook formats (electronic versus printed) on academic procrastination among secondary school students. This study emphasizes the importance of employing e-books instead of printed books as sustainable digital learning resources, thereby contributing to sustainable education and the preservation of natural resources. Furthermore, this research could serve as part of a broader series of studies investigating the effects of integrating sustainable digital resources into education on students’ psychological development, study habits, and educational outcomes.
... Metacognitive approaches are more favourable to boosting learning performance, according to prior studies (Cho & Heron, 2015;Goradia & Bugarcic, 2017) (Cho & Heron, 2015;Goradia & Bugarcic, 2017). Similarly, Goda et al. (2015) discovered that students with metacognitive abilities were better at managing their time and submitting assignments on time, resulting in increased learning performance. In the context of digital learning, however, some previous research argued that metacognitive strategies weaken control (Ackerman & Lauterman, 2012;Lauterman & Ackerman, 2014). ...
The primary objective of this paper is twofold; firstly, to analyse the relationship between metacognitive strategies and learning performance. Secondly, a new mediator is proposed, namely digital literacy. Mental resilience is an omnitemporal skill that enables individuals to gain resilience thinking to successfully adapt to life tasks. Although Researchers accentuate that metacognitive competency is necessary and crucial in enhancing mental resilience, research on inculcating resilience thinking through metacognitive skills is still undeveloped and warrants the urgent attention of the research community. Data was gathered from a cross-sectional study of 563 university students. To accomplish the goals of the research, data were analysed empirically utilising the variance-based partial least squares structural equation modelling (PLS-SEM) technique. The results revealed that learning performance was not significantly directly impacted by metacognition. This may be due to the fact that metacognitive strategies are used to help students to increase academic performance as opposed to learning capacity. However, through the mediation analysis, it was discovered that digital literacy was proven to be an effective mediator to promote learning performance. These new insights can assist academics to reflect and evaluate current practices to cultivate metacognitive practices in the classroom without having to alter the subject curriculum.
... Goda and others investigated the relationships among different learning types and ultimate learning outcomes to develop a support system for e-learning [8]. Taniguchi examined students' scores and how frequently they accessed content from multiple lectures to compare effective learning support methods [9]. LMS logs contain a large amount of data not directly related to learning; however, Lerche and Kiel reported how to use data mining to remove unnecessary logs and measure the learner's achievement activity compared with prior knowledge [10]. ...
... However, this autonomy challenges students to self-regulate their studying (Broadbent & Poon, 2015). For instance, online learners frequently fail to distribute their study time over the semester (Goda et al., 2015) which is associated with lower academic performance (Moon & Illingworth, 2005). Especially less conscientious students with lower academic competences struggle with the increased self-regulatory demands of online instruction (Credé & Kuncel, 2008;Hart, 2012). ...
The goal of the present study was to investigate how course instruction and individual differences in general academic competences and conscientiousness relate to students’ learning strategy use and exam performance. The sample comprised two cohorts of university students who attended a lecture on the same topic, but with varying course instruction: In the blended course (N = 238), the teacher applied deadlines for self-testing and offered regular in- class meetings to encourage distributed practice over the semester. In the online course, students studied independently without regular meetings, nor deadlines (N = 200). Learning strategies were measured objectively using behavioral log-file data. Students in the blended course used fewer self-tests than online students which was associated with poor exam performance. Academic competences (high school GPA) positively predicted exam performance via more distributed practice and self-testing. Conscientiousness was related to more distributed practice which was associated with better exam performance. Results revealed that (voluntary) in-class meeting and deadlines did not prevent cramming. Especially less conscientious students with lower general academic competences need further support in applying efficient learning strategies.
... Prior researchers have classified students based on digital strategy use (Bannert et al., 2017;Papamitsiou & Economides, 2014;Theobald, 2018;Wang et al., 2022), and indicate those who use effective learning strategies like distributed practice and self-testing with quiz questions were more likely to pass an online course (Kizilcec et al., 2013;Maldonado-Mahauad et al., 2018) relative to those who massed study (Davis et al., 2016;Goda et al., 2015) or who underutilized resources (Kizilcec et al., 2013;Maldonado-Mahauad et al., 2018;Papamitsiou & Economides, 2014;Theobald, 2018;Utz & Bernacki, 2018). Studies classifying students' strategy use provide real-world evidence of use of these strategies in authentic learning contexts. ...
Digital environments like learning management systems can afford opportunities for students to engage in cognitive learning strategies including preparatory reading of advance organizers including lecture outlines and self-testing using ungraded quizzes. When timed appropriately, self-testing can afford distributed practice, an optimal approach to self-testing that confers additional benefits. At a large, public university in the southwestern USA, we examined the frequency and timing of digital learning behaviors that reflect these practices in a large gateway science course and how these event types predicted exam performance of 220 undergraduates’ exam grades in the first unit of a 16-week anatomy and physiology course. Coursework over this 31-day span included lessons on cytology, histology, the integumentary system, and osteology; we observed the timing and frequency of students’ use of the lecture outline, ungraded self-testing quizzes, and hypothesized that those who self-regulated by downloading advance organizers before lecture (i.e., pre-reading) and utilizing quizzes to self-test (i.e., retrieval practice) and distributed this practice would achieve superior performances. Whereas students massed self-testing prior to the exam, a regression model that also included pre-reading, self-testing, and its distribution predicted achievement over and above massed practice. In authentic contexts, students used digital resources and benefitted from early lecture access or pre-reading advance organizers, and self-testing despite challenges to distribute practice and to self-test frequently and on recommended schedules.
... The Teaching and learning from home and the lack of social interaction amidst Covid-19 have induced stress and anxiety in emotional well-being amongst instructors and learners. As stated by Goda et al. (2015), building up stress and pressure backfires as we could see a lack of feedback from instructors, procrastination from learners and lack of engagement among instructorslearners. It is paramount that instructors and learners be given time to themselves during the weekend to take a break. ...
Time is a significant and massive worry in behaviour that causes difficulties amongst instructors and learners in ensuring an effective process of teaching and learning in open and distance learning (ODL) environment. This study aimed to identify instructors’ and learners’ ability to allocate time in ODL. The study was conducted with 320 respondents, and the instrument used was questionnaires. The results showed that ODL instructors and learners were challenged with many obstacles to prioritizing tasks and allocating time accordingly in their teaching and learning. A close inspection of the role of time management is much required. The most reported challenges were managing the academic schedule on ODL and multitasking while teaching and learning on ODL. Lastly, it was deemed unnecessary to have additional time to satisfy the expectations of ODL. It was found that both instructors and learners from public and private higher education institutions needed effective educational practices to succeed in ODL and blended learning. In short, time allocation in prioritizing tasks is still an obstruction to achieving effective and balanced teaching and learning system with ODL that satisfies the desire of the instructors and learners. This research suggests investigating social, cognitive, teaching and emotional presence to successfully conduct the teaching and learning process.
... The research conducted related E-Learning, one of them at a university in Japan that wanted to know the type of E-Learning learning habits of bachelor's degree students and investigate their relationship to their learning throughout the semester [17]. From the study of the first section of the 441 bachelor's degree students that have been analyzed, there are 7 types of habits that most identified: (1) delay, (2) learning habits, (3) random, (4) lack of motivation, (5) ready earlier, (6) the appropriate learning level, and (7) studied at the last minute. ...
E-Learning has become the latest teaching methods and slowly began to be implemented in various institutions in all corners of the world. Judging from its profits, E-Learning can be accessed by everyone using the internet, the material is instantly available and accessible online, saving the cost of study materials and no need to pay the shipping costs. It is becoming one of the factors that reinforce the use of E-Learning in Toastmasters International institution. The research will focus on proposed a model of factors influencing E-learning usability in Toastmasters International. The model will use based on if the following four factors namely Learning habits, User Satisfaction, Loyalty User, and Ease of User Interface has Usability effect on E-Learning in Toastmasters International. The results of this study suggest that four of these factors affect the usefulness of e-Learning. Therefore, the results of these recommendations will be recommended to the Toastmasters International for the development of E-Learning do better in the future.
... In the third and fourth hypotheses, it is hypothesized that justice experiences of university students would mediate, at least in part, the relationships between their PBJW and life satisfaction and academic cheating. During digital teaching, the established rules and norms of lecturers and their interactions with students provide basic information on their justice-related behaviors 48,[125][126][127][128] . Nevertheless, during digital teaching, face-to-face interaction as well as nonverbal communication, which is crucial for communicative understanding, is missing. ...
Is the belief in a just world among students also stable under COVID-19? To answer this question, a study was conducted with university students from Germany (n = 291). The aim of the study was to analyze the predictive performance of the personal belief in a just world (PBJW) on students' life satisfaction and academic cheating and to take into account important mediators from the university context such as fellow student justice, lecturer justice, and procrastination. Derived from existing research, university students with a stronger PBJW should be more satisfied with their lives and cheat less than those with a weaker PBJW. The results support the hypothesized direct effects of PBJW on life satisfaction. Procrastination additionally mediated the effect of PBJW on life satisfaction. The level of PBJW predicted academic cheating only indirectly. The mediators procrastination and lecturer justice were crucial here. The results persisted when gender, learning, time to exam, socially desirable responding, general BJW, and self-efficacy were controlled. The findings were discussed in relation to the stressful situation caused by COVID-19. A reflection on the adaptive function of PBJW as a resource and relevant situation-specific mediators for university research and practice followed.
... Furthermore, authors have conversed that educational institutions have invested in facilitated e-learning programs since the learning community is diverse and they demand a technology-based system (Massoud et al, 2011;Newman and Scurry 2001). Past studies have shown that e-learning tools to some extent have a significant impact on student learning outcomes and their performance (Ariana et al, 2016;Goda et al., 2015). ...
The technology-enabled technique has created a novel method in the teaching and learning atmosphere for students across the globe. E-learning tools enable students to learn at any time and place. The main purpose of this paper is to explore the literature study associated with the e-learning aspects and its impact on academic achievements at higher education institutions. The online database was explored and the literature study results indicated that the e-learning environment has a significant impact on student’s learning activities, academic productivity as well as performance. This paper examine the HEIs need to develop a comprehensive approach in providing an e-learning environment, that will have a potential impact on student engagement in learning and achieve higher educational outcomes.
... Research has shown that learners indeed make use of this opportunity and found great variety in the way learners study in online education: learners for instance differ in terms of the amount of material they complete, the (order of) activities they engage in, but also in the their forum activities, and in the timing of and time between their learning sessions (e.g., Goda et al., 2015;Jovanovi c et al., 2017;Kizilcec et al., 2013;Kovanovi c et al., 2015;Maldonado-Mahauad et al., 2018;Saint et al., 2018). Theoretically however, little is known about the origin of the variety in learner behaviour (Li & Baker, 2018). ...
Background
Learners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self‐regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching videos). The exact relationship between SRL and learner behaviour has however not been investigated.
Objectives
We explored whether differences in SRL are related to differences in learner behaviour in a MOOC. As insight in this relationship could improve our understanding of the influence of SRL on behaviour, could help explain the variety in online learner behaviour, and could be useful for the development of successful SRL support for learners.
Methods
MOOC learners were grouped based on their self‐reported SRL. Next, we used process mining to create process models of learners' activities. These process models were compared between groups of learners.
Results and conclusions
Four clusters emerged: average regulators, help seekers, self‐regulators, and weak regulators. Learners in all clusters closely followed the designed course structure. However, the process models also showed differences which could be linked to differences in the SRL scores between clusters.
Takeaways
The study shows that SRL may explain part of the variability in online learner behaviour. Implications for the design of SRL interventions include the necessity to integrate support for weak regulators in the course structure.
... However, the lack of supervision in online courses leads to frequent academic delays and less self-discipline among online learners [15]. In addition, by comparing seven different types of learning behaviour (academic procrastination, learning habit, random, diminished dive, early bird, chevron, and catch-up), some researchers have found that students with academic procrastination are significantly less effective in online learning than those with good learning habits, so it is difficult to achieve better grades in online classes [16]. On the other hand, the online teaching model often leads to an exponential increase in the amount of time students spend on their mobile phones or computers. ...
... La question critique de la validité des données et des pratiques basées sur ces données se pose face au caractère partiel et limité des traces numériques qui ne rend pas entièrement compte des pratiques d'apprentissage comme on pourrait par exemple les observer en classe (Peraya, 2019a ;2019b ;Pierrot, 2019). Face au développement des approches analytiques des traces d'apprentissage numériques, la littérature internationale soutient que de fausses interprétations concernant le comportement des apprenants sont possibles, par exemple lorsqu'on travaille avec la variable « temps de fréquentation » (Goda et al, 2015) ou « temps de réponse » (Kalman, Scissors, Gill & Gergle, 2013). Sans compter d'autres biais tels que ceux liés à l'absence de prise en compte de l'ensemble des apprenants en lien avec les inégalités d'accès au numérique (Tubaro, 2019), les biais liés à la fabrication par les apprenants eux-mêmes de traces ne correspondant pas à leur activité réelle et ceux liés aux problèmes d'authentification de l'identité de la personne et l'ajustement nécessaire du système de traces d'apprentissage à l'ingénierie de la formation comme peut en outre le proposer Moodle (Peraya & Luengo, 2019 ;Romero, 2019). ...
... Research has shown that learners indeed make use of this opportunity and found great variety in the way learners study in online education: learners for instance differ in terms of the amount of material they complete, the (order of) activities they engage in, but also in the their forum activities, and in the timing of and time between their learning sessions (e.g., Goda et al., 2015;Jovanovi c et al., 2017;Kizilcec et al., 2013;Kovanovi c et al., 2015;Maldonado-Mahauad et al., 2018;Saint et al., 2018). Theoretically however, little is known about the origin of the variety in learner behaviour (Li & Baker, 2018). ...
In online learning, students’ learning behavior might change as the course progresses. How students adjust learning behaviors aligned with course requirements reflects their self-regulated learning strategies. Analyzing students’ learning patterns can help instructors understand how the course design or activities shape students’ learning behaviors, including their learning beliefs and motivation, and facilitate teaching decision makings accordingly. This study aims to propose a scientific analytic method to understand students’ self-regulated learning (SRL) patterns. The whole process includes the following four steps: (1) encoding behavioral patterns; (2) detecting turning points and chunking behavioral patterns; (3) grouping similar patterns; and (4) interpreting results. A case study with 4604 K-12 students from 476 courses was conducted to validate the proposed method. Five successful patterns, three at-risk patterns, and three average patterns were identified. The case study indicated that successful students showed at least one of the following characteristics: (1) Balanced, (2) Proactive and Balanced, and (3) Balanced with one highly engaged behavior. The at-risk students showed the following characteristics: (1) Oscillatory and (2) Low Engaged. Patterns which led to successful or at-risk conditions are compared and connected with corresponding SRL strategies. Practical and research implications are discussed in the article as well.
Reading is considered as one of the most important language skill. In the reading comprehension process, students must understand the content of the text to get information from what they read. Metacognitive strategy, related to the students’ logical sequences, allows them to use effective ways to overcome the difficulties while they are reading. The aim of this study is to delve into metacognitive reading strategies used among senior high students for comprehending reading texts.The study suggests that students should develop positive attitudes towards reading comprehension process and teachers are required to integrate a metacognitive reading strategy instruction to foster students’ metacognitive strategies and comprehension abilities.
Background/Objective
In the post-epidemic era, an increasing number of individuals were accustomed to learning sports and physical activity knowledge online for fitness and health demands. However, most previous studies have examined the influence of e-learning materials and resources on learners and have neglected intrinsic factors such as experience and physiological characteristics. Therefore, we conducted a study to investigate the effect of exercise habits and gender on sports e-learning behavior via eye-tracking technology.
Methods
We recruited a sample of 60 undergraduate students (mean age = 19.6) from a university in Nanjing, China. They were randomly assigned into 4 groups based on 2 genders × 2 exercise habits. Their gaze behavior was collected by an eye-tracking device during the experiment. The cognitive Load Test and Learning Effect Test were conducted at the end of the individual experiment.
Results
(1) Compared to the non-exercise habit group, the exercise habit group had a higher fixation count (P<0.05), a shorter average fixation duration (P<0.05), a smaller average pupil diameter (P<0.05), and a lower subjective cognitive load (P<0.05) and better learning outcome (P<0.05). (2) Male participants showed a greater tendency to process information from the video area of interest (AOIs), and had lower subjective cognitive load (P < 0.05) and better learning outcomes (P < 0.05). (3) There was no interaction effect between exercise habits and gender for any of the indicators (P > 0.05).
Conclusion
Our results indicate that exercise habits effectively enhance sports e-learning outcomes and reduce cognitive load. The exercise habits group showed significant improvements in fixation counts, average fixation duration, and average pupil diameter. Furthermore, male subjects exhibited superior learning outcomes, experienced lower cognitive load, and demonstrated greater attentiveness to dynamic visual information. These conclusions are expected to improve sports e-learning success and address educational inequality.
In this paper, we present an approach for online course evaluation based on learners’ behaviors during the learning process, where the course creator can monitor the quality status of their online courses based on learners’ learning outcomes and then intervene to improve the success rate. For this purpose, a set of criteria has been developed. These criteria concern learners’ cognitive, affective and academic engagement. The proposed approach was adopted by a system consisting of four parts: a learning management system, a LOG preprocessing manager, a quality assessment manager, and a visualization and intervention manager. A first test was carried out on a sample of 33 students from the Department of Economics at the University of Guelma(Algeria), where the results obtained were very encouraging and promising.
This research is motivated by the still high incidence of academic procrastination among junior high school students. The purpose of this research is to examine whether academic self-efficacy and parental support contribute to students' academic procrastination. This research used a cross-sectional design, with a sample of 250 students selected through proportional stratified random sampling. The research instruments used included measurements of academic self-efficacy, parental support, and academic procrastination. Data were analyzed using multiple regression with the help of SPSS version 25.00. Research findings show that academic self-efficacy contributes negatively to academic procrastination (standardized beta = -.613, sig = .000), and parental support also shows a significant contribution (standardized beta = -.215, sig = .000). The implications of this research highlight the need to increase students' self-efficacy and parental support through a series of psychological interventions to reduce cases of academic procrastination among students.
Self-regulated learning (SRL) is a very important factor in students' learning process and in theirlearning environment. However, even with a well-developed learning plan, it is difficult to achieve learning goals without good time management. Previous research indicates that time management skills have a positive effect on learning performance. This study examined the effects of a learning time scheduling system that we developed on the enhancement of learning time management awareness, learning behaviors, and learning performance. The results indicate that most learners who did not use the scheduling function held to their deadline for weekly work submissions. However, the use of the scheduling function affects students' time management awareness, metacognition, and learning performance.
Procrastination is one of the issues affecting more than half of the student population and is known to impact them negatively. It is also one of the major reasons for failure and dropout. Therefore, several studies have been conducted in this domain to understand when and why students procrastinate. The existing studies use self-reported procrastination scales and/or digital traces of student interactions recorded in learning environments to identify procrastination behavior. The majority of the extant studies leverage individual tasks such as assignments submission, quizzes attempted, course materials assessed by a student, etc., to study such behavior. This paper uses group-based collaborative wiki activity to explore the procrastination behavior among the students. This study will help us explore student behavior in a group activity. The results would help us investigate if the student’s behavior changes when it comes to a group activity. The results would be beneficial for instructors, practitioners, and educational researchers to know if group activity could be utilized to overcome procrastination behavior.
Öğrenme deneyimlerinin dijitale geçmesiyle birlikte, öğrenenlerin bu ortamlarda gerçekleştirdikleri tüm etkileşimler izlenebilir hale gelmiştir. Bu da öğrenme analitiklerini hızla yaygınlaşmasına yol açmıştır. Öğrenme analitikleri, öğrenmenin kendisini ve öğrenme çevrelerini anlamak ve iyileştirmek için kullanılır. Öğrenenlerle ve öğrenme bağlamlarıyla ilgili verilerin toplaması, ölçülmesi, analiz edilmesi, görselleştirilmesi ve en önemlisi de analitik sonuçların öğrenme çevrelerine yansıtılması süreçleriyle ilgilenir. Öğrenme analitikleri, bireyin kendini izlemesine ve kendi performansını değerlendirmesine olanak sağladığı için öğrenenlerin öz düzenleyici öğrenmelerine de etki etmektedir. Alanyazında öğrenme analitikleri ile öz düzenleyici öğrenme ilişkisini inceleyen çalışmalar yer almaktadır. Bu araştırmada öğrenme analitiklerinin öz düzenleyici öğrenmeye etkilerini inceleyen çalışmalara yönelik bir sistematik alanyazın taraması yapılmıştır. Bu amaçla Web of Secience veri tabanı kullanılarak başlığında “learning analytic” ve “self-regulated” anahtar kelimeleri bulunan makaleler incelenmiştir. Çalışma sonucunda öğrenme analitikleri kullanmanın, öz düzenleyici öğrenme becerilerinin tespitinde ve geliştirilmesine yardımcı olduğu sonucuna ulaşılmıştır.
Students use self-regulated learning, a self-directed process, to evaluate their learning as they work toward
academic objectives. This study aims to determine the degree of self-regulated learning strategy in a flexible learning
environment with regard to affective, cognitive, metacognitive, and motivational strategy; ascertain the level of
students’ academic performance in flexible learning; assess if there is a significant relationship between the students’
academic performance and the self-regulated learning strategies; and find out which variable, singly or in
combination, predicts students’ academic performance. The study used a descriptive quantitative correlational
design. The study is conducted at Valencia National High School, Valencia City, Bukidnon. The research
respondents were 150 Grade 10 students. The reseachers used descriptive statistics to determine the level of each
sub-variable of self-regulated learning strategies and the students’ academic performance. The relationship involving
self-regulated learning and students' mathematical performance is examined using Pearson Product Moment
Correlation. Finally, to predict which sub-variables predict students’ academic performance, it utilizes regression
analysis. Results showed that students are indicated positive about self-regulated learning. The student's academic
performance is rated as very satisfactory. Henceforth, there is a significant correlation between students' academic
performance and their use of a self-regulated learning strategy. Lastly, the sub-variable that predicts students’
academic performance is the metacognitive strategy.
Procrastinação acadêmica refere-se ao atraso ou adiamento voluntário de atividades, mesmo que tal atitude cause dificuldades maiores a longo prazo. O presente estudo consiste em uma revisão sistemática da literatura para examinar a procrastinação acadêmica entre estudantes universitários brasileiros. Buscas foram realizadas em setembro de 2020 utilizando a expressão “procrastinação acadêmica” nas bases Periódicos CAPES, BVS, DOAJ, Scielo e Google Acadêmico. Foram selecionados 16 artigos empíricos cujo foco era a procrastinação acadêmica em estudantes universitários brasileiros. Verificou-se que procrastinação é um fenômeno frequente entre estudantes universitários e pode ser motivado por diferentes fatores (sentimentos de ansiedade e incapacidade diante da tarefa, percepção de dificuldade da tarefa, quantidade de tarefas a serem realizadas e significado atribuído à tarefa). Os resultados apresentados podem contribuir para subsidiar ações destinadas a auxiliar estudantes universitários a lidar com esse problema.
The procrastination assessment scale for students (PASS) has been used widely in evaluating the patterns of university students’ procrastination on academic tasks and their procrastination behavior. The present study validated the psychometric properties of a Chinese version of the PASS (PASS-C) by recruiting two representative independent sample of Hong Kong Chinese university students (S1 used in the EFA study: 506; S2 used in the CFA study: 506). The results confirmed that this modified Chinese version is a valid and appropriate tool to assess university students’ procrastination tendencies in Chinese educational settings.
This chapter applies data mining and learning analytics, along with self-regulated learning (SRL) theories, to examine possible interventions aimed at supporting students' success with online learning. The chapter introduces two learning support systems and the results of related research. These two systems are used as sample cases to describe the relationships among SRL, learning support, learning processes, and learning effects. Case 1 is an early warning system that uses an SRL questionnaire completed before actual learning to determine which students are likely to drop out. Case 2 focuses on student planning and the implementation phases of the SRL cycle. This system supports students' own planning and learning, creating distributed learning and reducing procrastination without human intervention. A comparison of the two cases implies that a combination of an early warning system and system constraints that require planning before actual learning can reduce the need for human learning support and decrease academic procrastination, resulting in increased distributed learning.
Because of the flexibility of online learning courses, students organise and manage their own learning time by choosing where, what, how, and for how long they study. Each individual has their unique learning habits that characterise their behaviours and distinguish them from others. Nonetheless, to the best of our knowledge, the temporal dimension of student learning has received little attention on its own. Typically, when modelling trends, a chosen configuration is set to capture various habits, and a cluster analysis is undertaken. However, the selection of variables to observe and the algorithm used to conduct the analysis is a subjective process that reflects the researcher’s thoughts and ideas. To explore how students behave over time, we present alternative ways of modelling student temporal behaviour. Our real-world data experiments reveal that the generated clusters may or may not differ based on the selected profile and unveil different student learning patterns.KeywordsLog dataTemporal student profileTemporal student segmentationTime-on-taskTemporal behaviour analysis
The COVID-19 crisis emphasizes the importance of Self-Regulated Learning (SRL), one of today’s most valuable skills, with which learners set their learning goals, monitor and control their cognition, motivation, and behavior, and reflect upon them. In the current experimental study, an intervention program based on short online interactive videos was developed to promote SRL skills. This paper presents the impact of the intervention on students’ use of SRL skills and grades. It also explores four key pedagogical processes (teacher-student relationships, collaboration, autonomy, and feedback) as mediators for SRL strategies use and grades. The experimental and control groups were randomly assigned (N = 290 students, 18 classes, grades 7–12). Each teacher taught the same subject in two classes for a month, an amount of time that allows intervention to take effect. One of the classes participated in the video-based intervention program (experimental group), whereas the other performed all activities but did not have access to the videos (control group). Data was collected through an SRL and pedagogies usage questionnaire, SRL video prompts, and knowledge tests and was analyzed using the quantitative method. In addition to the theoretical contribution, a practical tool has been developed for educators who wish to employ online SRL training.KeywordsSRL - Self-Regulated LearningVideo-assisted learningERT - Emergency remote teachingSRL intervention programCOVID-19
Students use self-regulated learning, a self-directed process, to evaluate their learning as they work toward academic objectives. This study aims to determine the degree of self-regulated learning strategy in a flexible learning environment with regard to affective, cognitive, metacognitive, and motivational strategy; ascertain the level of students' academic performance in flexible learning; assess if there is a significant relationship between the students' academic performance and the self-regulated learning strategies; and find out which variable, singly or in combination, predicts students' academic performance. The study used a descriptive quantitative correlational design. The study is conducted at Valencia National High School, Valencia City, Bukidnon. The research respondents were 150 Grade 10 students. The reseachers used descriptive statistics to determine the level of each sub-variable of self-regulated learning strategies and the students' academic performance. The relationship involving self-regulated learning and students' mathematical performance is examined using Pearson Product Moment Correlation. Finally, to predict which sub-variables predict students' academic performance, it utilizes regression analysis. Results showed that students are indicated positive about self-regulated learning. The student's academic performance is rated as very satisfactory. Henceforth, there is a significant correlation between students' academic performance and their use of a self-regulated learning strategy. Lastly, the sub-variable that predicts students' academic performance is the metacognitive strategy.
Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system.
Investigated the frequency of 342 college students' procrastination on academic tasks and the reasons for procrastination behavior. A high percentage of Ss reported problems with procrastination on several specific academic tasks. Self-reported procrastination was positively correlated with the number of self-paced quizzes Ss took late in the semester and with participation in an experimental session offered late in the semester. A factor analysis of the reasons for procrastination Ss listed in a procrastination assessment scale indicated that the factors Fear of Failure and Aversiveness of the Task accounted for most of the variance. A small but very homogeneous group of Ss endorsed items on the Fear of Failure factor that correlated significantly with self-report measures of depression, irrational cognitions, low self-esteem, delayed study behavior, anxiety, and lack of assertion. A larger and relatively heterogeneous group of Ss reported procrastinating as a result of aversiveness of the task. The Aversiveness of the Task factor correlated significantly with depression, irrational cognitions, low self-esteem, and delayed study behavior. Results indicate that procrastination is not solely a deficit in study habits or time management, but involves a complex interaction of behavioral, cognitive, and affective components. (16 ref) (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Researchers and practitioners have long regarded procrastination as a self-handicapping and dysfunctional behavior. In the present study, the authors proposed that not all procrastination behaviors either are harmful or lead to negative consequences. Specifically, the authors differentiated two types of procrastinators: passive procrastinators versus active procrastinators. Passive procrastinators are procrastinators in the traditional sense. They are paralyzed by their indecision to act and fail to complete tasks on time. In contrast, active procrastinators are a "positive" type of procrastinator. They prefer to work under pressure, and they make deliberate decisions to procrastinate. The present results showed that although active procrastinators procrastinate to the same degree as passive procrastinators, they are more similar to nonprocrastinators than to passive procrastinators in terms of purposive use of time, control of time, self-efficacy belief, coping styles, and outcomes including academic performance. The present findings offer a more sophisticated understanding of procrastination behavior and indicate a need to reevaluate its implications for outcomes of individuals.
The purpose of this research was to investigate the relationships between students' help seeking target types and e-learning focusing on students' completion rate and satisfaction. In this research, 292 students in e-learning courses at a university in Japan were categorized into 4 types of help seeking target; (1) Unnecessary of help, (2) Necessary of help, Action (Formal Target), (3) Necessary of help, Action (Informal Target), and (4) Necessary of help, No action. The overall completion rate averaged .843 and the overall satisfaction ranging from 1 (Not satisfy at all) to 4 (Very satisfy) averaged 2.96. For data analyses, overall MANOVA with two dependent variables, completion rate and satisfaction, was significant (Λ=.929, F(6, 574)=3.567, p=.002). ANOVA was conducted for each dependent variable. The results showed that there was a statistical significance between help seeking types and satisfaction (F(3, 288)=5.669, p=.001), although no significance was found for the completion rates (F(3, 288)=1.995, p=.115). The results indicated that Type (3) may have positive effects on satisfaction in e-learning. This means that students who could use other resources as well as formal target such as teachers or mentors may actively engage their learning and lead to positive affective reaction after the course. The research findings should provide significant information to teachers, administrators, and researchers of e-Learning to plan and provide effective and appropriate helps to learners.
The present study focused on analyzing the factors of procrastination and its effects on learning of university students. It was conducted on 500 students and 40 teachers of the Islamia University of Bahawalpur, Pakistan through survey approach. The study concluded that procrastination effects on the academic performance of students in terms of classroom learning and participation in activities, submission of their assignments, preparing for the examinations and achievement. Likewise, the work load of assignments' and improper time management by the students caused procrastination.
Procrastination is an educational concern for classroom instructors because of its negative psychological and academic impacts on students. However, the traditional view of procrastination as a unidimensional construct is insufficient in two regards. First, the construct needs to be viewed more broadly as time-related academic behavior, encompassing both procrastination and timely engagement. Secondly, the underlying motivation of these behaviors needs to be considered. Therefore, we developed and validated a 2 × 2 model of time-related academic behavior. The results of a confirmatory factor analysis supported a four-factor structure, and correlation with a unidimensional measure of procrastination also supported this model. Furthermore, the 2 × 2 model demonstrated significantly better fit to the data than potentially competing models. Structural equation modeling with achievement goals revealed that the 2 × 2 model unveiled relationships previously obscured in the traditional model, including that procrastination appeared to be used as a performance-enhancing strategy, while timely engagement was used to enhance mastery. The theoretical and practical implications of these new relationships are discussed.
The aim of this study was to examine the effect of procrastination on students’ life satisfaction among a group of college students. In this regard, Tuckman Procrastination Scale and Satisfaction with Life Scale were administered to 314 (214 female, 100 male) college students. The average age of the participants was 20.76 (SD=1.97) with an age range between 17 and 27. The results of the preliminary analysis showed that 38% (119) of the students claimed to be frequent procrastinator, with male students reporting more frequent procrastination than female students do. Results of the ANOVA yielded a significant difference for academic procrastination level on satisfaction with life score. Specifically, procrastinators reported to have lower life satisfaction score than do non-procrastinators.
This study investigates the effect of procrastination on academic performance. Prior research has often relied upon self-reported measures of procrastination, which are only weakly correlated with actual procrastination. We use the start and submission of a set of online homework problems as two objective, direct measures of student procrastination and the grade on the assignments as a measure of performance. In our study, there were a number of potential benefits to submitting online assignments ‘just-in-time’. Thus, there was a direct benefit to procrastination, which students had to weigh against potential drawbacks. With a sample size larger than those previously reported in the literature, we find that for both procrastination measures, task procrastination is associated with lower task performance. To ensure that our results are not just an association between performance and student quality, we test for the association between task procrastination and task performance, while controlling for student quality. We find that even after controlling for student quality, task procrastination is associated with lower task performance.
Procrastination is variously described as harmful, innocuous, or even beneficial. Two longitudinal studies examined procrastination among students. Procrastinators reported lower stress and less illness than nonprocrastinators early in the semester, but they reported higher stress and more illness late in the term, and overall they were sicker. Procrastinators also received lower grades on all assignments. Procrastination thus appears to be a self-defeating behavior pattern marked by short-term benefits and long-term costs.
Describes how motivational processes influence a child's acquisition, transfer, and use of knowledge and skills. Recent research within the social-cognitive framework illustrates adaptive and maladaptive motivational patterns, and a research-based model of motivational processes is presented that shows how the particular performance or learning goals children pursue on cognitive tasks shape their reactions to success and failure and influence the quality of their cognitive performance. Implications for practice and the design of interventions to change maladaptive motivational processes are outlined. It is suggested that motivational patterns may contribute to gender differences in mathematics achievement and that empirically based interventions may prevent current achievement discrepancies and provide a basis for more effective socialization. (79 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Investigated the frequency of 342 college students' procrastination on academic tasks and the reasons for procrastination behavior. A high percentage of Ss reported problems with procrastination on several specific academic tasks. Self-reported procrastination was positively correlated with the number of self-paced quizzes Ss took late in the semester and with participation in an experimental session offered late in the semester. A factor analysis of the reasons for procrastination Ss listed in a procrastination assessment scale indicated that the factors Fear of Failure and Aversiveness of the Task accounted for most of the variance. A small but very homogeneous group of Ss endorsed items on the Fear of Failure factor that correlated significantly with self-report measures of depression, irrational cognitions, low self-esteem, delayed study behavior, anxiety, and lack of assertion. A larger and relatively heterogeneous group of Ss reported procrastinating as a result of aversiveness of the task. The Aversiveness of the Task factor correlated significantly with depression, irrational cognitions, low self-esteem, and delayed study behavior. Results indicate that procrastination is not solely a deficit in study habits or time management, but involves a complex interaction of behavioral, cognitive, and affective components. (16 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Academic procrastination includes failing to perform an activity within the desired time frame or postponing until the last minute activities one ultimately intends to complete. As such, high levels of procrastination appear inconsistent with the behaviors typically attributed to self-regulated learners, However, research exploring the relation between these 2 constructs is lacking. Two studies (N = 168 and N = 152) examining procrastination and its relation to several key components of self-regulated learning using self-report surveys are reported here. Results indicate that procrastination was related to college students' self-efficacy and work-avoidant goal orientation and, to a lesser extent, their use of metacognitive strategies. Findings are discussed with regard to prior research on self-regulated learning and procrastination and to future research. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students' learning behaviors in an online business course in Taiwan. The study scientifically identified students' behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers.
This study examined correlates of academic procrastination and students’ grade goals in a sample of 226 undergraduates from
Singapore. Findings indicated that self-efficacy for self-regulated learning was significantly and negatively related to procrastination.
High self-efficacy for self-regulated learning also predicted students’ expectations of doing well and low self-efficacy for
self-regulated learning predicted students’ expectations of not doing well academically. Additionally, help-seeking predicted
students’ expectations of doing well academically while academic stress predicted students’ expectations of not doing well
academically. Implications for education and educational practice were discussed.
P class=abstract>This study reviewed the distance education and self-regulation literatures to identify learner self-regulation skills predictive of academic success in a blended education context. Five self-regulatory attributes were judged likely to be predictive of academic performance: intrinsic goal orientation, self-efficacy for learning and performance, time and study environment management, help seeking, and Internet self-efficacy. Verbal ability was used as a control measure. Performance was operationalized as final course grades. Data were collected from 94 students in a blended undergraduate marketing course at a west coast American research university (tier one). Regression analysis revealed that verbal ability and self-efficacy related significantly to performance, together explaining 12 percent of the variance in course grades. Self-efficacy for learning and performance alone accounted for 7 percent of the variance.
Keywords: self-regulated learning, blended learning, online learning</P
Researchers and practitioners have long regarded procrastination as a self-handicapping and dysfunctional behavior. In the present study, the authors proposed that not all procrastination behaviors either are harmful or lead to negative consequences. Specifically, the authors differentiated two types of procrastinators: passive procrastinators versus active procrastinators. Passive procrastinators are procrastinators in the traditional sense. They are paralyzed by their indecision to act and fail to complete tasks on time. In contrast, active procrastinators are a "positive" type of procrastinator. They prefer to work under pressure, and they make deliberate decisions to procrastinate. The present results showed that although active procrastinators procrastinate to the same degree as passive procrastinators, they are more similar to nonprocrastinators than to passive procrastinators in terms of purposive use of time, control of time, self-efficacy belief, coping styles, and outcomes including academic performance. The present findings offer a more sophisticated understanding of procrastination behavior and indicate a need to reevaluate its implications for outcomes of individuals.
Procrastination is a prevalent and pernicious form of self-regulatory failure that is not entirely understood. Hence, the relevant conceptual, theoretical, and empirical work is reviewed, drawing upon correlational, experimental, and qualitative findings. A meta-analysis of procrastination's possible causes and effects, based on 691 correlations, reveals that neuroticism, rebelliousness, and sensation seeking show only a weak connection. Strong and consistent predictors of procrastination were task aversiveness, task delay, self-efficacy, and impulsiveness, as well as conscientiousness and its facets of self-control, distractibility, organization, and achievement motivation. These effects prove consistent with temporal motivation theory, an integrative hybrid of expectancy theory and hyperbolic discounting. Continued research into procrastination should not be delayed, especially because its prevalence appears to be growing.
This study compared students' academic procrastination tendency with the (1) frequency and nature of rationalizations used to justify procrastination, (2) self-regulation, and (3) performance in a web-based study strategies course with frequent performance deadlines. 106 college students completed the 16-item Tuckman Procrastination Scale, a measure of tendency to procrastinate, the Frequency of Use Self-survey of Rationalizations for Procrastination, and a 9-item self-regulation scale. Students' subsequent course performance was measured by total points earned. A linear regression with Academic Procrastination as the criterion variable and Rationalization score and Course Points as the predictor variables suggested academic procrastinators support procrastinating by rationalizing, not self-regulating, and thus put themselves at a disadvantage, with respect to evaluation in highly structured courses with frequent enforced deadlines.
Academic procrastination includes failing to perform an activity within the desired time frame or postponing until the last minute activities one ultimately intends to complete. As such, high levels of procrastination appear inconsistent with the behaviors typically attributed to self-regulated learners. However, research exploring the relation between these 2 constructs is lacking. Two studies (N = 168 and N = 152) examining procrastination and its relation to several key components of self-regulated learning using self-report surveys are reported here, Results indicate that procrastination was related to college students' self-efficacy and work-avoidant goal orientation and, to a lesser extent, their use of metacognitive strategies. Findings are discussed with regard to prior research on self-regulated learning and procrastination and to future research.
Studies have long shown that students who begin or submit their work later tend to have negative academic outcomes. The measures of procrastination used in those studies may not have provided information timely enough for instructor intervention. This article focuses on delay in the online environment among graduate students. We propose two new measures of delay that can be disclosed in a timely manner, enabling instructors to help students who are prone to late submissions to succeed. Date of class registration and date of initial class posting are negatively associated with final letter grades. Date of first class posting can serve to alert instructors to those with potential delay problems. The results for date of class registration are less clear.
Procrastination, putting off until tomorrow what should have been done today, is a self-regulation failure that is widespread among students. Although plenty of research has emerged regarding academic procrastination, hardly any research endeavor regarding procrastination in distance university settings exists. This lack of research is even more astonishing when considering that the demands on self-regulation are higher in distance education settings than in traditional university settings. The present (questionnaire) study was intended to shed light on procrastination in an actual distance university setting by exploring its relationship to grades, learning strategies (e.g., cognitive, meta-cognitive strategies), and life satisfaction in students from a distance university in comparison to students from a traditional university.
Using a series of computer-based assignments, we examined whether students’ submission patterns revealed a hyperbolic pattern of temporal discounting, such that few assignments are submitted far ahead of the deadline and submission of assignments accelerates at an increasing rate as the deadline becomes imminent. We further examined whether variables related to self-regulation – namely, self-reported procrastination, implementation intentions, say-do correspondence, and perceived academic control – correlated with behavioural postponement. Results revealed strong behavioural evidence of temporal discounting, especially among those who identified themselves as procrastinators. Among the self-regulation measures, only say-do correspondence consistently correlated with procrastination.
A growing body of research suggests that academic procrastination is a dynamic behavior that follows a curvilinear trajectory over time. In this research, we examined whether there are inter-individual differences in this trajectory, the extent to which these differences can be predicted by other variables, and the relationship between temporal changes in procrastination and academic outcomes. We collected multi-wave data from 303 students regarding their actual procrastination behavior and test performance during an academic semester, as well as single measurements of their self-reported levels of trait procrastination, conscientiousness, and neuroticism. Using latent growth curve modeling, we found that high and low procrastinators followed the same trajectory over time, that the self-report measures did not predict temporal changes in procrastination and test performance, and that procrastination behavior was negatively related to test performance throughout the semester. The implications of these findings for trait-based theories of procrastination, and the measurement of procrastination in general, are discussed.
The purpose of this study was to develop a self-report measure of procrastination tendencies and to investigate its relationship to a behavioral measure of procrastination and to a self-report measure of general self-efficacy. In a pilot study, the 72-item scale in a 4-point Likert-type response format was developed and administered to 50 college juniors and seniors. A factor analysis of the results yielded two factors which formed the basis for reducing the scale to 35 items with a resulting reliability of .90. The relationship between scores on the 35-item instrument and performance on a self-regulated performance task called the Voluntary Homework System (VHS) yielded a correlation of -.54, and a coefficient of -.47 was observed between the 35-item scale and the General Self-Efficacy Test (GSE; both correlations of p < .001). The correlation between GSE and VHS scores was .29 (p < .05). In a subsequent study of 183 college students, a factor analysis of scores on the 35-item scale yielded a single-factor structure and a condensed scale of 16 items with a reliability of .86. This shortened version of the procrastination scale was recommended for use as a means of detecting students who may tend to procrastinate in the completion of college requirements.
Abstract Self-regulated learning concerns the application of general models of regulation and self- regulation to issues of learning especially within academic contexts. Self-regulated learning is an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained
This paper considered three studies designed to examine procrastinatory behavior. In Study I, a general form (G) of a true-false procrastination scale was created. This form was based on an earlier version of the scale containing parallel forms A and B. Procrastination was positively related to measures of disorganization and independent of need-achievement, energy level, and self-esteem. High scorers on the procrastination scale were more likely to return their completed inventory late. Procrastination was unrelated to grade-point average (R = −10). In Study II, subjects completed Form G of the procrastination scale and a variation of Little's (1983) Personal Projects Questionnaire. Based on ratings of their personal projects, procrastinators and nonprocrastinators were distinguished in a number of ways, foremost being the nonprocrastinator's more positive response to the project dimension of stress and the procrastinator's greater sensitivity to how enjoyable the project was in terms of time spent. In Study III, after completing a personality inventory, air-passengers awaiting their flight departure were asked to take an envelope with them and to mail it back on a designated date. Procrastinators were less accurate in doing so than were nonprocrastinators. Various aspects of procrastinatory behavior were discussed, including a reconsideration of the defining of the construct.
The present study focuses on a specific learner characteristic in the management of time – procrastination-, and its role in an online learning environment. More specifically, it was expected that procrastination would influence the successfulness of online learning and that this could be explained by the level of participation of learners in discussion forums. A study was conducted to test this hypothesis among a sample of learners taking a 10-week course on environmental and land use issues. As predicted, a negative relationship was found between procrastination and performance, and this relationship was mediated by the level of the learners’ participation in discussion forums. In other words, it appears that if high procrastinators are less successful online learners than low procrastinators, it is partly due to their lack of participation in discussion forums during the learning process. Additionally, some behavioral differences between high and low procrastinators were found in the times they decided to (re)start working at a distance, felt motivated to work on their course, and felt like dropping out of the course. To conclude, some practical implications for tutoring online activities and for stimulating participation in online learning environments have been proposed.
Procrastination has been studied as a dysfunctional, self-effacing behavior that ultimately results in undesirable outcomes. However, A. H. C. Chu and J. N. Choi (2005) found a different form of procrastination (i.e., active procrastination) that leads to desirable outcomes. The construct of active procrastination has a high potential to expand the time management literature and is likely to be adopted by researchers in multiple areas of psychology. To facilitate the research on this new construct and its further integration into the literature, the authors developed and validated a new, expanded measure of active procrastination that reliably assesses its four dimensions. Using this new measure of active procrastination, they further examined its nomological network. The new 16-item measure is a critical step toward further empirical investigation of active procrastination.
This study compared students' academic procrastination tendency with the (1) frequency and nature of rationalizations used to justify procrastination, (2) self-regulation, and (3) performance in a web-based study strategies course with frequent performance deadlines. 106 college students completed the 16-item Tuckman Procrastination Scale, a measure of tendency to procrastinate, the Frequency of Use Self-survey of Rationalizations for Procrastination, and a 9-item self-regulation scale. Students' subsequent course performance was measured by total points earned. A linear regression with Academic Procrastination as the criterion variable and Rationalization score and Course Points as the predictor variables suggested academic procrastinators support procrastinating by rationalizing, not self-regulating, and thus put themselves at a disadvantage, with respect to evaluation in highly structured courses with frequent enforced deadlines.
Effects of academic procrastination on college students' life satisfaction
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Overcoming procrastination: Or how to think and act rationally in spite of life's inevitable hassles
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