Learning Management System Data Research Articles

"The Design and Acceptability of a Hip Hop Themed Integrated Nutrition Math Curriculum for Minoritized 5th Grade Students Using the Multisensory Multilevel Health Education Model".

Digital technology creates new opportunities to design multisensory learning experiences. Evidence suggests that digital innovation can greatly benefit health education, including nutrition programs. The COVID-19 pandemic disrupted the education sector, forcing schools to modify standard practices from exclusively in-person delivery to online or blended learning. Digitalized curriculums became particularly useful as an Emergency Remote Teaching tool. This article focuses on developing and implementing a multimedia, multisensory, and scalable Hip-Hop Healthy Eating and Living in Schools (H.E.A.L.S.) Nutrition-Math Curriculum (NMC). NMC comprises 20 lessons-music-based multimedia resources used in the classroom or at home. Fourteen lessons represent self-directed online modules (asynchronous learning) hosted on a Learning Management System (LMS) called "Gooru." The remaining six lessons are teacher-facilitated (in person or using Zoom) review sessions (synchronous learning). The article discusses (1) the development of NMC through the lens of the Multisensory Multilevel Health Education Model (MMHEM), (2) the high acceptability of NMC evaluated using a mixed-methods design among minoritized fifth-grade students attending an after-school program, and (3) the students' completion and mastery rates of the NMC modules based on LMS data. Multimedia nutrition education programs integrated with common core curriculum content, such as NMC, may be a promising avenue for disseminating health education to minoritized children living in New York City and similar high fast-food density cities.

Identifying Student Behavior Patterns Based on LMS Data

In contemporary education, data analysis methods to establish patterns of student behavior for subsequent optimization of the educational process are becoming increasingly relevant. This paper presents research aimed at identifying patterns of student behavior based on the processing of data obtained from Learning Management Systems (LMS). The article examines data collection and analysis issues from LMS, including activity logs, grades, participation in forums, and other interactive elements. Additionally, methods such as statistical analysis and machine learning, applied to identify patterns of student behavior, are discussed. The text describes the identified patterns of student behavior in the electronic educational environment, which are subsequently linked to students' academic performance levels. The paper's concluding section presents the research findings and potential scenarios for their application. Key Words: adaptive learning, machine learning, learning management systems, student behavior patterns.

Generating personalised profiles of student engagement to predict student performance and support student learning using LMS data

With the rising popularity of delivering educational content in the online space, this study set out to better understand how students are increasingly engaging with Learning Management Systems (LMSs) for their learning (Gupta, Muralidharan, & Raghavan, 2021; Henrie, Halverson, & Graham, 2018; Conijn, Snijders, Kleingeld, & Matzat, 2017). A data mining approach was used to investigate whether student activity data from LMSs could be used, first in generating profiles of student engagement and, second, in predicting student academic performance (Romero & Ventura, 2013; Tempelaar, Rienties, & Giesbers, 2015; Conijn et al., 2017). LMS activity data was collected throughout a semester for a cohort of undergraduate students (N=534) enrolled in a biomedical science subject. This activity data comprised of total views and downloads of lecture recordings and average time spent viewing lecture recordings, total number of page clicks, total number of LMS course and content page clicks (e.g., lecture lessons), total number of discussion forum posts and views, and total number of formative quiz attempts and reviews (Wang, Chen, & Anderson, 2019; Conijn et al., 2017). K-means cluster analysis was performed to generate profiles of student engagement using this digital LMS data. Measures of student activity derived from this digital LMS data were used to predict academic performance (students' final unit totals), using linear regression and ANOVA. We demonstrate the utility of LMS log data in constructing 'profiles' of student engagement, and in establishing predictors of academic performance. In line with existing literature, average time spent viewing lecture recordings was determined to be a significant predictor of academic performance. Clustering analyses revealed three distinct clusters, or ‘profiles’ of student engagement, each representing key differences in student engagement activity, with greater homogeneity in student engagement behaviour within clusters, and greater heterogeneity across clusters. The current findings provide valuable insights on how our students are increasingly engaging with LMSs for their learning. As delivery of education towards the online space continues to gain momentum, evidence-based approaches such as those utilized in this study will help inform and enhance teaching practices (Jovanovi?, Gaševi?, Pardo, & Dawson, 2020; Ullmann, Wild, & Scott, 2018; Conijn et al., 2017). A better understanding of how students are differentially engaging with LMSs will also help support the development of teaching resources that more closely mirror and complement online student learning behaviors (Dyckhoff, Zielke, Bültmann, Chatti, & Schroeder, 2013; Conijn et al., 2017).

Learning analytics with iLearn Insights

iLearn Insights is an innovative online application to address the challenge of engaging, motivating and supporting students with automated personalised email for those at risk of failure. It allows academics to identify students who are engaging or otherwise with the Learning Management System (LMS) content, thus allowing early intervention, and ultimately improved retention in units by providing targeted support to a student cohort. In contrast to systems that provide only static data reports, this vendor-independent innovation was built in-house to analyse and visualise student learning data in relation to access patterns, forum activity, learning activity submission and grades to identify a student cohort that needs support, as well as enabling teaching staff to send personalised emails to students on the basis of their level of engagement and/or performance. Feedback has a powerful impact on learning, but students frequently highlight it as an area that can be improved in tertiary education (Dawson and Henderson, 2019). iLearn Insights provides graphical representation of LMS data that enables visual personalised feedback to students. Within three mouse clicks, academic staff can trigger a range of automated communications to commend high-achieving students, offer additional assistance to lower performing students or to recapture disengaged students. These emails can be a targeted to a group of students and configured by the unit convenor to contain motivating information including the top five resources accessed by classmates; a student’s mark in comparison to the class average; number or percentage of students that have already submitted an assignment; clickable links; and support resources for students falling behind. ILearn Insights was developed based on four principals of learning analytics design knowledge: integration, agency, reference frame and dialogue (Wise, 2014). It has been observed that targeted visual feedback with clickable links is the most effective way to engage students quickly.?The positive impact of iLearn Insights is demonstrated by its rapid uptake by teaching staff across Macquarie University. When it launched in Session 1, 2020, after 18 months of piloting, there were 478 users across 763 units (subjects) who sent 125334 targeted personalised emails. In Session 1, 2023 iLearn Insights was used by approximately 808 users across 40 departments and learning support areas and 1237 units, sending over 316576 targeted emails to encourage students to engage with learning activities or offer support. That represents an increase of 169% of users, 162% of units and 253% of emails in three years. These personalised email exchanges have led to enhanced student engagement, which is critical for student success (Kahu and Nelson, 2018; De Villiers and Werner, 2018; McClenney et al, 2012; Klem and Connell, 2004). 

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

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

Utilization of Learning Management Systems (LMS) Salam Al Azhar as the Learning Process in Al Azhar 35 Islamic International Primary School

This paper explores the use of Learning Management Systems (LMS) in a primary school setting, focusing on Al Azhar 35 Islamic Primary School. LMS offers numerous advantages over traditional learning systems, and its application in primary schools is relatively rare in Indonesia. The study employs the Process Mining Project Methodology (PM2) to analyze LMS data, assess its readiness, and examine its suitability for process mining. The study identifies structured data with relevant Case ID, Activity, and Timestamp fields, indicating the data's suitability for process mining. The frequently observed LMS activities include Dashboard, Course Overview, and My Course. These activities are analyzed to assess the LMS's practicality, efficiency, and advantages. The findings reveal that LMS is beneficial for both teachers and students. The study concludes that PM2 methodology is a suitable approach for process mining in education, and the data from Al Azhar 35's LMS is ready for analysis. Future research opportunities include analyzing the learning processes in more diverse case studies, improving data quality, and exploring learning patterns in various classes, semesters, and courses.

Predicting time‐management skills from learning analytics

AbstractBackgroundTechnological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing timely interventions.ObjectivesWhile most learning analytics studies focus on using LMS data to predict performance, this study instead predicts students' self‐reported time‐management skills using trace data from the Canvas LMS. This is done for courses at one Dutch technical university with in total 462 students.MethodsLinear regression and multi‐level regression models are constructed using both theory and findings from previous research. The predictions made by these models are compared to previously filled in questionnaire data to validate the results.ResultsOur results show that models can be constructed and time‐management can be predicted for individual courses. Furthermore, there are several predictors that are significant in multiple models. However, these models and predictions are not immediately transferable to other courses.ConclusionsThe study therefore emphasizes the need for further research, using multiple sources of data or more theoretically grounded predictors, to investigate the extent of the portability issues with these predictive models. Despite this we were able to predict the students' self‐reported time‐management skills in multiple different courses using Learning Analytics, and managed to identify multiple consistently predictive trace data variables.

Using Explainable Machine Learning to Automatically Provide Feedback to Students Based on Data Analysis

Providing feedback to students is one of the most powerful practices that have enhanced education in the world today. Despite there being useful feedback provided by students’ self-regulation and teachers’ feedback provision, there is still a need for feedback that provides meaningful insights or actionable information about the reasons behind it, which is not provided by the said feedback. This paper explores how we can use explainable machine learning to compute data-driven feedback concerning students’ academic performance and generate actionable recommendations which are beneficial for students and teachers. This method has been developed based on LMS (Learning Management System) data from a university course. The effectiveness of the proposed approach has been evaluated with the results demonstrating 90% accuracy.
  

Predictive Identification of At-Risk Students: Using Learning Management System Data

This paper describes a neural network model that can be used to detect at-risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail a specific course. Data from the grade books from all course shells from the Spring 2022 semester (N = 22,041 rows) were analyzed, and four factors were found to be significant predictors of student success/failure: the current course grade after the fifth week of the semester and the presence of missing grades in weeks 3, 4, and 5. Several models were investigated before concluding that a neural network model had the best overall utility for the purpose of an early alert system. By categorizing students who are predicted to fail more than one course as being generally at risk, we provide a metric for those who use early warning systems to target resources to the most at-risk students and intervene before students drop out. Seventy-four percent of the students whom our model classified as being generally at risk ended up failing at least one course.

APPROACH TO THE IMPLEMENTATION OF A COMPETENCY-BASED APPROACH IN THE LEARNING MANAGEMENT SYSTEM

This paper solves the urgent problem of improving the tracking of the process of acquiring competencies by students through the means of analyzing the learning management system data and visualizing the results of the learning process. The object of the study is to track the process of acquiring competencies by students. The subject of the study includes software components for the implementation of a competency-based approach in the learning management system. The purpose of the study is to improve the tracking of the process of acquiring competencies by students by analyzing the data of the Learning Management System (LMS) and visualizing the results of the learning process. Thus, to achieve this goal, we analyzed the technologies for processing learning data from LMS, analyzed the features of modeling the learning process based on Petri nets and BPMN (Business Process Model and Notation), determined a data structure, and proposed an algorithm for building a model and visualizing the learning process data. The developed software components allow processing of learning process data from LMS, building models and visualizations of learning process data, saving the results to data warehouses, depicting the learning process model, and visualizing learning process data for further analytical processing.

Data-Based Learning Analysis and Its Application to English Education: Focusing on Online and Blended Learning Environment

This study uses a portion of the learner data from Learning management system(LMS) to find factors that affect academic achievement. In addition, this study tried to suggest a method to apply this learning analysis to English education. For this purpose, the LMS data for (1) lectures during the 4th semester (September 2020~June 2022) in the ‘online class environment’ due to COVID-19 and (2) classes in the 2nd semester in 2022 conducted as ‘blended learning’ classes was collected and analyzed. The main results are as follows. In the ‘online learning environment’, it was found that there was a significant positive correlation between the access frequency and grades by LMS content and the frequency of access to class lecture videos, notices and assignments had a significant effect on learners’ grades. In the ‘blended learning environment’ the results of the correlation analysis showed that there was a significant positive correlation between the access frequency of announcements and videos and grades.
 However, in the results of the regression analysis, there were no variables that affected grades. This study is meaningful in that it investigated the relationship between the frequency of access to each content and academic achievement in order to identify more accurate variables.

Changes in online course designs: Before, during, and after the pandemic

The switch to emergency remote teaching during the COVID-19 pandemic became for many university instructors a necessity to familiarise themselves with the institution’s online learning management system (LMS). This switch to online teaching made learning activities, course design patterns, and pedagogical approaches more visible than during face-to-face teaching. Furthermore, the process of translating physical learning activities to the digital space was challenging and needed institutional and leadership support. This paper presents an analysis of the changes made to the learning designs of 102 courses in a university’s LMS before, during, and after the pandemic. Using descriptive statistics and Epistemic Network Analysis we used LMS data to explore the use of LMS features revealing not only the overall university trends, but also faculty differences. In addition, we compared the learning activities as described in course descriptions with the actual learning activities designed in the LMS. Our findings show that although the switch to emergency remote teaching forced many instructors to change the learning designs of their courses, some instructors reverted to their pre-pandemic learning designs after the pandemic, while other instructors did not change their learning design during the pandemic at all. In addition, we identified a positive trend of an increased use of activity features in the learning management system. Finally, we reflected on the importance of the university leadership supporting the digital transformation.

Using LMS Log Data to Explore Student Engagement with Coursework Videos

Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students’ engagement with online videos. Learning analytics data from the MoodleTM and Vimeo® platforms were compared. The research also examined the impact of video length on engagement, and how engagement with videos changed over the course of a semester when multiple video resources were used in a course. The comparison in platform learning analytics showed differences in metrics thus offering a caution to users relying on unidimensional metrics. While the results support the notion that log data do provide educators with an opportunity for review, the time and expertise in extracting, handling, and using the data may stifle its widespread adoption.

Standing on the shoulders of giants: Online formative assessments as the foundation for predictive learning analytics models

AbstractAs universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data‐driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data‐driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections of the course. The number of clicks in the LMS and the time difference between first access and due dates of formative assessments were also significant predictors. Overall, our findings emphasize the need for online formative assessments to build predictive LA models informed by theory and learning design. Practitioner notesWhat is already known about this topic Higher education institutions often use learning analytics for the early identification of low‐performing students or students at risk of dropping out. Most predictive models in learning analytics rely on immutable student characteristics (e.g., gender, race and socioeconomic status) and complex variables extracted from log data within a learning management system. Prioritizing prediction accuracy without theory orientation often yields “black‐box” models that fail to inform educators on what remedies need to be taken to improve student learning. What this paper adds Predictive models in learning analytics should consider learning theory, pedagogy and learning design to identify key predictors of student learning. Online formative assessments can be a starting point for building predictive models that are not only accurate but also provide educators with actionable insights on how student learning can be improved. Time‐related and score‐related features extracted from online formative assessments are particularly useful for predicting students' course performance. Implications for practice and/or policy This study provides strong evidence for using online formative assessments as the foundation for predictive models in learning analytics. Student data from online formative assessments can help educators provide students with feedback while informing future formative assessment cycles. Higher education institutions should avoid the hype around complex data from learning management systems and instead rely on effective learning tools such as online formative assessments to revolutionize the use of learning analytics.

Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work

Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer behaviors to behaviors collected from smart-home devices. Increasingly, higher education systems have been using Learning Management System (LMS) digital data to capture and understand students’ learning and well-being. Researchers in the social sciences are increasingly interested in the potential of using digital log data to predict outcomes and design interventions. Using LMS data for predicting the likelihood of students’ success in for-credit college courses provides a useful example of how social scientists can use these techniques on a variety of data types. Here, we provide a primer on how LMS data can be feature-mapped and analyzed to accomplish these goals. We begin with a literature review summarizing current approaches to analyzing LMS data, then discuss ethical issues of privacy when using demographic data and equitable model building. In the second part of the paper, we provide an overview of popular machine learning algorithms and review analytic considerations such as feature generation, assessment of model performance, and sampling techniques. Finally, we conclude with an empirical example demonstrating the ability of LMS data to predict student success, summarizing important features and assessing model performance across different model specifications.

The impact of extreme weather on student online learning participation

In March 2020, the COVID-19 pandemic forced over 1 billion learners to shift from face-to-face instruction to online learning. Seven months after it began, this transition became even more challenging for Filipino online learners. Eight typhoons struck the Philippines from October to November 2020. Two of these typhoons caused widespread flooding, utilities interruptions, property destruction, and loss of life. We examine how these severe weather conditions affected online learning participation of Filipino students pursuing their undergraduate and graduate studies. We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle Learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes was not significant. These findings suggested that students continued to participate in activities that have a direct bearing on their final grades, rather than activities that had no impact on their course outcomes.

A Partnership-Based Approach to Operationalizing Learning Behaviours Using Event Data

This paper describes a partnership-based approach for analyzing data from a learning management system (LMS) used by students in grades 6–12. The goal of the partnership was to create indicators for the ways in which students navigated digital learning activities, referred to as playlists, that were comprised of resources, pre-assessments, and summative assessments. To develop various indicators, the collaboration gathered school practitioners’ perspectives on desirable and undesirable student actions within and across playlists, jointly explored and made sense of LMS data, and examined the relationships between behavioural indicators and outcomes that were important to practitioners. The approach described in this paper is intended to provide an example for future researcher–practitioner collaborations to build upon when seeking to jointly analyze data from digital learning environments. The widespread use of playlists and LMSs in K–12 schools throughout the United States means that the collaborative process described in this paper may have broad applicability to large numbers of digital environments, schools, and collaborations.

Use of Learning Management System Data to Predict Student Success in a Pharmacy Capstone Course

Objective. Learning management system (LMS) data from online classes may provide opportunities to identify students at risk of failure. Previous LMS studies have not addressed the possibility of change in student engagement over time. The purpose of this study was to apply a novel statistical technique, group-based trajectory modeling (GBTM) to LMS data in an online course to identify predictors of successful course completion.Methods. Exploratory GBTM was used to assess the association of LMS activity (total activity time, dates of activity, and pages viewed) and attendance at virtual synchronous learning sessions with examination performance in a capstone disease-management course delivered in the final didactic quarter of a three-year Doctor of Pharmacy program. Groups were assigned based on trajectories of weekly page view counts using structural-equation modeling.Results. Group-based trajectory modeling identified three page view engagement groups (median total page views, n): group 1, high (1,818, n=24): group 2, moderate (1,029, n=74), and group 3, low (441 views, n=35). Group assignment alone was somewhat associated with final grade. Stratification based on consistent virtual synchronous learning session attendance improved predictive accuracy; for example, a top (A or A-) grade was earned by 49.0% and 24.0%, respectively, of group 2 students with and without consistent synchronous engagement.Conclusion. Application of GBTM to LMS data, including information about synchronous engagement, could provide data that allow educators to identify early warning signs that a student may fail a course and target interventions to those at-risk students. The technique should be further tested with alternative LMS data and obtained early in the didactic curriculum, before patterns of engagement are established.

A literature review on the use of retrospective LMS data to investigate online Teaching and Learning practices

Access to high quality education is a cornerstone of social, cultural, and economic recovery after any crisis. This is also true of the global COVID-19 pandemic which has disrupted the pedagogical practices of higher education institutions around the world (Butler-Henderson, Crawford, Rudolph, Lalani, & Sabu, 2020). Digital learning has become the new-norm, and tertiary education institutions have been propelled to innovate their teaching methods by integrating digital learning through the adoption of cost-effective (Al-Maroof & Al-Emran, 2018) and adaptable (Bhat et al., 2018) Learning Management Systems (LMS) (Bervell & Umar, 2017; Cigdem & Ozturk, 2016). In these systems, pedagogical technologies are embedded in an infrastructure that enables administration and management of learning contents, communication, assessment, and collaboration (Washington, 2019). Due to the fact that the fundamental task of LMS is to support digital teaching and learning (El Bahsh & Daoud, 2016), they are some of the most extensively used learning technologies in higher education (Abazi-Bexheti, Jajaga, & Abazi-Alili, 2018). LMS technology is now widely adopted to support face-to-face, blended, and online pedagogical practices. In this context, LMS data provides large-scale capture, processing, and analysis of students’ interactions with the system, with each other, and with their teaching support within the system (Chung, 2014).  LMS data provides a rich resource through which to investigate online learning technologies and behaviours (see, for example, Chung, 2014). Such work is increasingly being done across multiple domains, however no systematic review has yet been conducted which surveys such work - specifically examining retrospective LMS data. This is the goal of the present paper.
  
 A literature review was conducted to examine what data analysis methods have been used to better understand online pedagogy. The review focused specifically on the use of tertiary level retrospective LMS data and was not limited to a specific academic domain. The review was conducted from January - December 2021, with searching being conducted in January 2021. A total of 97 full text articles were included in the literature review. The literature review aimed to identify the kinds of research questions retrospective LMS data is being used to answer, the analytical techniques used to analyse this data, and the types of study designs used in this field of research. The number of students represented in the data and the academic domains were also considered. Quality of data and analytical reporting was assessed in order to interrogate the opportunities and challenges of reproducible research in studies using retrospective LMS data. Finally, the review considered the degree to which the analysis of retrospective LMS data met the needs of the research question.
  
 Understanding how retrospective LMS data has been used to examine pedagogical practice in previous research equips us to reorientate Teaching and Learning in the immediate aftermath of COVID-19. This will become increasingly important as we move towards a future characterised by an escalation of remote and online learning opportunities. Through surveying previous research in this area, this paper provides an important foundation for future work utilising retrospective LMS data to understand online Teaching and Learning in the peri-COVID era.
  
 References
  
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Looking closely at what they say and what it tells us: Experiences in a digital learning space

This study explores lecturer and student experiences of using the Learning Management System (LMS) within a Faculty at a South African University. The results of the study highlight the extent to which lecturers and students engage with the LMS. This article aims to determine the following: 1) the value that lecturers and students place on using the LMS as a platform to facilitate learning and teaching, 2) the typical resources, activities and assessments that lecturers and students place value on and why, and 3) to compare lecturer and student perspectives on the best utilization of the LMS. Quantitative data were collected from the LMS and qualitative data collected from lecturers and undergraduate students through questionnaires and focus groups. A Social Constructivist framework was adopted as a lens for analysis of collected data. The results show resources are valued the most by both lecturers and students but the majority of students only access, on average, just more than half of the postings. In terms of the constructs of the Social Constructivist framework, Learning and Connectedness showed positive responses, while improvement is necessary for Making Meaning and Agency. Keywords: agency, connectedness, digital learning space, Learning Management System, making meaning, learning opportunities, value