Systematic literature review on time management of educational activities in learning management systems

El uso de sistemas de gestión del aprendizaje es cada vez más frecuente como modalidad de enseñanza-aprendizaje. Esto se debe al hecho de que permite una mayor flexibilidad de tiempo y espacio en relación con el aprendizaje presencial. Por lo tanto, este trabajo tiene como objetivo presentar cómo las áreas de minería de datos educativos y análisis de aprendizaje están contribuyendo a extraer conocimiento de la autorregulación de la gestión del tiempo en entornos de e-learning Para esto, consideramos el concepto de gestión del tiempo de Pintrich (2000) y llevamos a cabo una revisión sistemática de la literatura. Fue posible evidenciar que la mayoría de los trabajos analizados no tienen como objetivo investigar sobre la gestión del tiempo, a pesar de que informan sobre resultados. También se observa que los datos que representan la gestión del tiempo, utilizados en la investigación, son datos agregados, es decir, el fenómeno no se estudia con el tiempo. Con estos resultados, tenemos una visión general de cómo el campo de Learning Analytics y Educational Data Mining están contribuyendo a extraer conocimiento sobre la autorregulación de la gestión del tiempo en entornos en línea.The use of learning management systems is becoming frequent as a form of learning. This is because it allows greater flexibility of time and space if we compare to face-to-face learning. Thus, this work aims to present how the field of Educational Data Mining and Learning Analytics are contributing to the extraction of knowledge from the self-regulation of time management in e-learning environments. For this, we considered the concept of time management by Pintrich (2000) and carried out a systematic review of the literature. With that, it was possible to notice that most of the analyzed works do not study only time management. We also realized that the data, which represents time management, are aggregated data, that is, the phenomenon is not studied over time. With these results, you can see an overview of how Learning Analytics and Educational Data Mining are supporting the extraction of knowledge about self-regulation of time management in online environments.O uso de sistemas de gerenciamento da aprendizagem vem se tornando frequente como forma de ensino-aprendizagem. Isto se deve ao fato dele possibilitar maior flexibilidade de tempo e espaço em relação à aprendizagem presencial. Assim, este trabalho tem por objetivo apresentar como as áreas de Mineração de Dados Educacionais e Learning Analytics estão contribuindo para extração de conhecimento da autorregulação da gestão de tempo em ambientes de e-learning. Para isso, consideramos o conceito de gestão de tempo de Pintrich (2000) e realizamos uma revisão sistemática de literatura. Com isso, foi possível perceber que a maioria dos trabalhos analisados não objetivam pesquisar sobre a gestão de tempo, ainda que reporte resultados sobre. Também percebemos que os dados, que representam a gestão de tempo, utilizados nas pesquisas são dados agregados, isto é, o fenômeno não é estudado ao longo do tempo. Com estes resultados tem-se uma visão geral de como o campo de Learning Analytics e a Mineração de Dados Educacionais estão contribuindo para extração de conhecimento sobre autorregulação da gestão de tempo em ambientes online.peerReviewe

Embedding Online Based Learning Strategies Into the Engineering Technology Curriculum

Various blended learning strategies have been implemented at engineering technology programs to facilitate different learning styles and different time constraints given to faculty. Some of these efforts are related to the effective use of online tools such as online course management systems, ePortfolios, narrated presentations, web-based polling systems, tutorials and educational materials posted before the class and asynchronous learning methods. As technology changes, some of the online learning methods are getting more advanced which is enabling more innovative approaches and data compression. Various distance learning programs started with having access to videos of recorded lectures (on VHS tapes, or CDs) and further they went to use of new media which followed the use of online based strategies such as online management systems, use of social media, podcasts, and other means of communication to deliver the instruction. It became easier to share videos to a wider audiences and enable easier access to state of the art in development in new engineering areas. Accessing pre-recorded educational modules is now easier with new wireless gadgets, with widespread networking capabilities on campuses and outside the campus. In this way, students have opportunities to spend more time in interacting with faculty in class, not only in their assigned office hours. These teaching and learning methods are emphasizing a not so new educational principle, the Socratic method. This concept is especially important for universities with diverse student population which include working adult student population, students who are with the military, students who have families and all other which are non-traditional students who do not live on campus. In this paper, embedding online based learning strategies into the classroom efforts in Engineering Technology department at one midsize institution is discussed

E-Learning. A study of students’ attitudes and learning outcome when using blended learning with integration of multimedia instructions

The advent of new technologies has provided opportunities and challenges for education institutions to seek more effective ways of teaching and learning. Elearning is now an established phenomenon in education and institutions are increasing their effort to offer greater flexibility, more personalized learning, and greater learner satisfaction. Consequently blended learning has emerged as a delivery method that addresses the face-to-face advantages of a traditional classroom and the time efficiency and location convenience of an online teaching and learning environment. The opportunities of flexibility and convenience are not evident in a classroom environment. However the face-to-face interactions provide the foundation for social communication which can be critical to online learning. Educators who are comfortable with traditional classroom delivery will meet learners’ enhanced demands for flexibility through online presence for courses. As educators are generally time-pore, and with little clear direction, research can give them valuable insights into advantaged and disadvantaged of various teaching and learning formats. This thesis examines students’ attitudes when using blended learning with integration of multimedia instructions. It identifies aspects around cognitive learning outcome and provides insight into students’ experiences and their overall satisfaction with this instructional design. The main objective with this initiative is to sustain the evolution from traditional teaching to active learning and to better integrate the increasing number of educational resources online. In particular this study includes aspects about students’ attitudes towards using a blended learning format, learning theories, the instructional principles of multimedia production, and identification of optimal ways to use e-learning. This thesis contributes to the field of e-learning by three main contributions (A1-A3): A1: A contribution utilizing blended learning with integration of multimedia instructions. The quality of the blended learning format is discussed on the basis of the attitudes and experiences from the adult participants. The contribution outlines characteristics about good properties of multimedia instructions to supplement traditional classroom teaching. The content of A1 constitutes a paper that is submitted for journal publication. A2: A model for testing the cognitive learning outcome using a blended learning format with two different teaching treatments. A2 constituted the main part of a conference proceedings paper. The study further contributes in a book with the title: Cases on managing e-learning: Development and implementation. Will be released in 2012. A3: This contribution outlines a blended learning course design for postgraduate dental students with emphasis on flexibility and location convenience. The course was redesigned from a former traditional course format. The content of A3 constitutes a paper that is submitted for journal publication. The research makes a contribution in the exploration of the advantages and disadvantaged of utilizing blended learning. The research methods comprise both quantitative and qualitative investigation approaches. The empirical data for this thesis were collected through 149 participating students and 13 semi-structured interviews. The thesis supports the view of increased favorable ways of teaching and learning when using new online technologies. However no evidence for increased cognitive learning outcome was identified. Nonetheless the blended learning format with integration of multimedia instructions holds an experienced potential for improved quality of teaching and learning in terms of enhanced satisfaction among learners. The main findings. The thesis contributes to the field of e-learning by the following main contributions. First, an identification of educational key issues favourable to a blended learning format with integration of multimedia instructions; second, identification of factors to produce high quality multimedia instructions; third, the design of a test procedure to conduct measurements on cognitive learning outcome based on a basic retention level and a more advanced transfer level; forth, suggestions how to improve the influence of the internet media for future postgraduate dental educational programs

Supporting E-Learning Within a Social Framework

E-learning has become a major delivery platform for higher education, continuing education and corporate training. The majority of e-learning research to date has taken place in academic environments using survey or qualitative research. This study used an experimental design (N = 99) to view three different models for supporting asynchronous e-learning in a corporate setting where learners are geographically distributed. The support interventions were rooted in andragogical principles of learning. Two treatment groups were provided socially engaging proactive models while the control group used a learner directed reactive authoritarian model. The purpose of this study was to see if different variables influenced trainee completion time and retention at six months of employment for new female branch administrators in a financial services company. The goal was to produce a predictive model for employee training completion and retention based upon type of e-learning support and other demographic and observed variables. Data analysis used multiple regression to determine if training completion time could be predicted. There was no significant relationship between any of the variables and time to completion. Logistic regression was used to model prediction of trainees most likely to stay on the job at six months. The only variable approaching significance from that analysis was gender of supervisor. Neither regression analysis resulted in a valid predictive model. This study used an available voluntary sample randomly assigned to treatment groups and tracked through training by dedicated support specialists trained in the different interventions. A larger sample size and different methods of treatment implementation should be studied with this population in the future

Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.Comment: 43 pages, 9 figures, 18 tables, Journal of Educational Data Mining (Initial Submission

Using Admissions Data to Create a First-Semester Academic Success Model

Higher education is attracting more students from diverse background especially at public community colleges. These institutions can help these students attain a quality education at a reasonable price. Unfortunately, community colleges have lower graduation rates than 4-year institutions in part due to the diverse needs and variety in academic preparedness amongst their populations. It can be difficult to identify students most at risk of performing poorly until it is too late. There are multiple ways to predict students’ performance. In this study, three common data mining techniques are compared for their accuracy in predicting academic success using only data collected at the point of admissions. Accurate early prediction can allow academic support professionals to intervene and provide intrusive assistance. A neural network model was found to be more accurate than logistic regression and decision tree models. Moreover, data elements of high school GPA, age, and sex were the most important factors in the neural network model

Exploring Relations Between Motivation, Metacognition, and Academic Achievement Through Variable-Centered, Person-Centered and Learning Analytic Methodologies

The three studies that comprise this dissertation examine relations between student characteristics, motivations, metacognitive learning processes, and academic achievement. Methodologically, the dissertation demonstrated the potential of multiple types of approaches and data resource types. By employing multiple approaches including variable-centered, person-centered, and learning analytics, researchers can understand learning processes from various angles. In addition, through this triangulation by multiple types of methodological approaches, educational theories could be more thoroughly verified and supported by various empirical findings. Multiple types of data resources are related to analytical methods. The purpose of the first paper was to examine relations between achievement goals and metacognitive learning behaviors using a clustering analysis and visualization. A clustering analysis conducted with achievement goals produced three goal profiles; 1) mastery-approach, 2) performance-approach, and 3) performance-avoidance identified three goal profiles. The profiles include High Approach, High Mastery, and High Goal Endorsement groups. The finding demonstrated that students in the High Mastery group, who had greater use of the self-assessment tool, obtained higher final grades than other groups could be explained from the perspective of SRL. In addition, learners motivated by mastery approach goals engaged in the greater use of self-assessment quizzes. Students in the High Mastery group also used the tools earlier than other two groups for exam 2. As the most frequently used pattern, sequential pattern mining discovered the repeated use of self-assessment quizzes to monitor their learning. More students in the High Mastery group employ this pattern of metacognitive events than students in the High Performance and High-Goal endorsement groups, particularly during sessions in weeks before exams. A subsequent analysis revealed that for all exams, students who conducted a repeated behavior pattern indicative of metacognitive monitoring and control outperformed those who did not. From the research, it is confirmed that the person-centered analysis provided authentic and generalizable groups and afforded observation of the learning behaviors of learners with typical combinations of goals. In addition, sequential patterns provide instructor more interesting information on learning processes than the frequency of accesses. The purpose of the second research was to identify motivational profiles based on multiple types of motivations including self-efficacy, achievement goals, and expectancy-value from an integrative perspective. For this research, a LPA was conducted with ten types of motivational constructs and three kinds of metacognitive learning processes. The LPA identified four motivational profiles; 1) High Cost, 2) High Performance Goals, 3) High Goals and Values, and 4) Low Performance Goals, and three metacognitive profiles; 1) Infrequent metacognitive processing. 2) Checking performance and planning, and 3) Self-assessment. Student demographic information significantly influenced the membership of motivational profiles. Older students tend to have higher self-efficacy, mastery-approach, and values, but low cost than younger ones. In addition, compared to Caucasian and Asian students, underrepresented students tend to be more motivated by higher goals and values than high cost or high performance goals. Lastly, female students are more likely to be members of High performance goals and High goals and values than High cost oriented and Low performance goals and cost than males. In terms of the relations profiles with academic achievement, Low Performance Goals group showed the best performance. Among metacognitive profile groups, students in Checking performance and planning, and Self-assessment demonstrated similar academic performance. The investigation of relations between two profile groups demonstrated that students in the High cost group are more likely to be a member of self-assessment group than checking performance and planning as well as of a member of an infrequent metacognitive process than checking performance and planning. In addition, students in high performance and goals and high goals and values groups relative to the low performance goals group more likely to be a member of the infrequent metacognitive process than checking performance and planning. The findings of this research provide authentic motivation status and metacognition learning process as well as their relations. Addition, this research figured out specific motivational profiles through the multiple types of motivations from the integrative perspective. Therefore, instructors can provide more effective and specific interventions to students who have difficulty utilizing metacognitive learning processes, considering motivational status based on multiple motivations. In addition, instructors can understand motivational profiles by demographics so at the beginning of the semester in which the information on students is not enough to identify students learning processes, they intervene students based on demographic information. The purpose of the third paper was to consider the relative importance of capturing demographic, motivational and metacognitive processes as potential predictors of learning outcomes, and appraises them alongside both traditional prediction modeling approaches in higher education, and emergent methods, sequence pattern mining, arising from the field of educational data mining. The sequence pattern mining discovered the repeated use of self-assessment quizzes in Biology and repeated use of planning contents in Math. A regression model with combined resource types demonstrated the improved predictive power than models with individual resource types. Also, theory-aligned behaviors designed based on metacognitive learning processes better improved the accuracy of the model than non-theory-aligned behaviors automatically provided by the system. Lastly, when applying the same prediction model, the model better explained the variance of academic achievement in Biology in which metacognitive supporting tools designed based on an educational theory than that in Math that has few theory-aligned behavior variables. Therefore, this study emphasizes the importance of existing ambient data from university systems. Also, log data generated by systems such as LMS allows researchers to examine the same data in different ways with no need for additional data collection. Lastly, educational theory and contexts should be taken into consideration in designing courses and developing the prediction models. Therefore, instructors and researchers, in designing courses, the consideration of educational theories and contexts is the essential process. This dissertation provides insight regarding authentic relations between motivation, metacognition, and academic achievement. Specifically, instructors can understand how multiple types of motivations work together, and the motivational profiles influence metacognitive learning strategies. In courses, by examining motivational profiles, instructors can provide more effective intervention with which students change their resolve their weak learning easier. Practically, by investigating each type of predictor from data resources including demographic, motivation, and behavioral variables, findings from this dissertation can enable researchers to prioritize development of prediction models to identify students who are more likely to experience failure in courses. Additionally, instructors can figure out the importance of interpreting variables through educational theories and in context through the comparison of courses with differing instructional designs. Further, by appraising these results in light of theory, instructors can take action to improve student’s learning outcomes by adjusting the design of their courses