The University of Texas at Arlington is a state university located in Arlington, Texas. The campus is situated southwest of downtown Arlington, and is located in the Dallas–Fort Worth–Arlington metropolitan area
The demand for data science and learning science skills has continued to increase as classrooms, labs, and organizations look to optimize their data and improve learning environments for students and employees. The UTArlingtonX Learning Analytics courses will give you the opportunity to gain invaluable knowledge and expertise in this growing field.In this introductory course, you will develop a solid understanding of fundamental learning analytics theories and processes, and explore different types of educational data. You will gain experience working with educational data sets and the R programming language, and hear from a diverse set of voices in the field. Finally, you will also consider ethics and privacy issues, as well explore how to work as part of a team in a domain that is becoming increasingly cross-disciplinary.By grasping these fundamental areas, you will have a better understanding of the field of learning analytics and be able to apply skills to any occupation that utilizes educational data.
Capturing and analyzing data has changed how decisions are made and resources are allocated in businesses, journalism, government, and military and intelligence fields. Through better use of data, leaders are able to plan and enact strategies with greater clarity and confidence. Data drives increased organizational efficiency and a competitive advantage. Simply, analytics provide new insight and actionable intelligence.In education, the use of data and analytics to improve learning is referred to as learning analytics. Analytics have not yet made the impact on education that they have made in other fields. That’s starting to change. Software companies, researchers, educators, and university leaders recognize the value of data in improving not only teaching and learning, but the entire education sector. In particular, learning analytics enables universities, schools, and corporate training departments to improve the quality of learning and overall competitiveness. Research communities such as the International Educational Data Mining Society (IEDMS) and the Society for Learning Analytics Research (SoLAR) are developing promising models for improving learner success through predictive analytics, machine learning, recommender systems (content and social), network analysis, tracking the development of concepts through social systems, discourse analysis, and intervention and support strategies. The era of data and analytics in learning is just beginning.Data, Analytics, and Learning provides an introduction to learning analytics and how it is being deployed in various contexts in education, including to support automated intervention, to inform instructors, and to promote scientific discovery. Additionally, we will discuss tools and methods, what skills data scientists need in education, and how to protect student privacy and other rights. The course will provide a broad overview of the field, suitable for a broad audience. Learners will explore the logic of analytics, the basics of finding, cleaning, and using educational data, predictive models, text analysis, and activity graphs and social networks. We will discuss use of analytics in data domains such as log files and text data. Tableau Software is partnering with University of Texas Arlington to provide analytics software to course participants as well as technical support and guest lectures. Additional software will be introduced and discussed throughout the course.How this course works:This course will experiment with multiple learning pathways. It has been structured to allow learners to take various pathways through learning content - either in the existing edX format or in a social competency-based and self-directed format. Learners will have access to pathways that support both beginners, and more advanced students, with pointers to additional advanced resources. In addition to interactions within the edX platform, learners will be encouraged to engage in distributed conversations on social media such as blogs and Twitter.
In this course, you will learn the basics of cluster analysis, one of the most popular data mining methods for the discovery of patterns in learning data, and its application in learning analytics.Cluster analysis enables the identification of common, archetypal patterns of student interactions, which can lead to better understanding of student learning behaviors and provision of personalized feedback and interventions.This course will have a strong hands-on component, as you will learn how to conduct a cluster analysis using the popular Weka data mining toolkit.We will cover K-means and Hierarchical clustering techniques, which are two simple, yet widely used, cluster analysis methods. We will also review some of the published learning analytics studies that adopted cluster analysis and learn how to interpret the cluster analysis results.Finally, we will also examine some of the more advanced techniques and identify certain practical challenges with cluster analysis, such as the selection of the optimal number of clusters and the validation of cluster analysis results.Week 1: IntroductionLectures:Introduction to unsupervised machine learning methodsIntroduction to clusteringOverview of clustering uses for learning analyticsLabs:Introduction to Weka toolkitWeek 2: Overview of k-means and hierarchical clustering methodsLectures:K-means clustering theoryK-means full exampleHierarchical clustering theoryHierarchical clustering full exampleLabs:Conducting k-means clustering using WekaConducting hierarchical clustering using WekaWeek 3: Practical considerationsLectures:How to choose the number of clustersHow to interpret clustering resultsOverview of more advanced clustering methodsLabs:Real-world cluster analysis walkthrough
This course will benefit educational designers, learning technology managers, and academics that are interested in how to use data to guide the design and improvements of a learning experience.Technology has the ability to collect a large amount of data about how people participate in a learning experience. How can this data be used to increase our understanding of how learning occurs? How can data be translated into actionable knowledge? How can data help improve the overall quality of a learning experience? These are the questions that are explored during the activities in the course. You will need basic knowledge about data manipulation and statistical analysis, and you will learn how to use them to translate data into actionable knowledge to apply in a learning experience.
Want to learn how to integrate technology into your classroom? This education and teacher training course takes us to the intersection of research and actual classroom practice. It brings together thought leaders, campus leaders, and practicing teachers to provide a practical framework for integrating technology into K12 teaching and learning.
This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining. It will cover the concepts and techniques that underlie current educational “student success” and “early warning” systems, giving you insight into how learners are categorized as at-risk through automated processes.You will gain hands-on experience building these kinds of predictive models using the popular (and free) Weka software package. Also, included in this course is a discussion of supervised machine learning techniques, feature selection, model fit, and evaluation of data based on student attributes. Throughout the course, the ethical and administrative considerations of educational predictive models will be addressed.Week 1: PredictionPredictive models vs. explanatory modelsThe predictive modeling lifecyclePredictive models of student successEthical considerations with predictive modelsOverview of the state of the practice in educational predictive modelsWeek 2: Supervised LearningSupervised machine learning techniques, including Decision Trees and Naive BayesWeek 3: Model EvaluationMaking predictionsModel evaluation and comparisonPractical considerations
Globally, higher education institutions are grappling with the effects of an increasingly digital world. This course provides an overview of how digitization impacts the economics, administration, academics, and research practices of universities and colleges. Higher education administrators, faculty, staff, and students will benefit from this course by exploring the emerging structure and role of higher education.Participants will engage with key trends that are shaping higher education and learn how these trends are amplified by the growing digital structure of society. In order to prepare their institution to become digital, learners will go through processes of evaluating trends, determining impact on education practices, and finally produce a strategic department or organizational planning document.
Concerned about how the digital age is impacting your well-being? Looking for ways to find balance? This course takes the ancient practice of yoga and translates it into modern day science with practical applications.You will learn how to practice yoga on the mat as well as in your everyday life using aspects of yoga that are immediately applicable to you. Having taught yoga to thousands of people just like you, we have reduced the practice down to the nectar of what really works.Your team of instructors brings a dynamic blend of science and practice to the course. Stacy and Dave Dockins own four yoga studios in Texas and have trained hundreds of instructors to teach yoga as a life-transforming practice rooted in mindfulness. Dr. Catherine Spann and Dr. George Siemens are researching what it means to be human in a digital age at the University of Texas at Arlington’s LINK Research Lab. With years of experience in online education and psychological research, they bring expertise in learning and well-being in the digital age.This course is for anyone interested in learning the science and practice of yoga. No previous yoga experience is needed! We welcome those who are interested in learning the basics of yoga postures as well as experienced yoga practitioners or instructors looking to deepen their practice.By signing up for this course, you will have the opportunity to meet and discuss yoga and meditation with people from across the world. Encourage friends, family, and colleagues to sign up with you!
In this course, participants will explore research-informed, effective practices for online teaching and learning. By enrolling, you will learn practical ways to quickly move into teaching online, guided by top scholars and practitioners in the field. Each module, you will watch videos and read articles by online learning experts and participate in activities and discussions covering critical topics that will make the online environment a rich learning experience for your students. The instructors will synthesize relevant resources to help those who are new to online learning and those who have experience, but want to expand their skills and provide support for others. You will have the opportunity to ask questions, share practices that have worked well in online learning environments, and receive feedback on your teaching and learning plans. Given recent global developments related to COVID-19, many have rapidly shifted to move teaching online. For those who have not taught online before, this can be a challenging experience. Fortunately, there is a rich research base, dating back over sixty years, that provides insight and guidance on the key factors that enable successful learning online. This course will support the pivot to online learning by exploring the scientific literature as well as practical actions that enable online success and equitable outcomes for all learners.While the target audience of the course is postsecondary institutions, this course will be of use to anyone moving into online teaching and learning.
The goal of this mathematics course is to provide high school students and college freshmen an introduction to basic mathematics and especially show how mathematics is applied to solve fundamental engineering problems. The aim of the course is to show the students why mathematics is important in an engineering career by demonstrating how simple engineering problems can be mathematically described and methodically analyzed to find a solution.A number of applied examples from various engineering disciplines will be introduced, analyzed and solved.
Interested in learning a computer programming language but unsure of how and where to begin? This course, Learn to Program Using Python, is a great place to start.Python is an easy and fun language to learn, and it is now one of the most popular programming languages, suitable for almost any task from developing graphical user interfaces to building web applications.This course is an introduction to the Python programming language. This course is open to all learners who wish to gain an understanding of the basic components of computer programming. You will learn basic computer programming concepts and terminologies such as variables, constants, operators, expressions, conditional statements, loops, and functions. This Python course includes hands-on exercises to help you understand the components of Python programming while incrementally developing more significant programs. The exercises in this course will be based on small assignments which will relate to real-world problems.No previous programming knowledge needed.
Interested in learning a computer programming language but unsure of how and where to begin? This course, Learn to Program Using Python, is a great place to start.Python is an easy and fun language to learn, and it is now one of the most popular programming languages, suitable for almost any task from developing graphical user interfaces to building web applications.This course is an introduction to the Python programming language. This course is open to all learners who wish to gain an understanding of the basic components of computer programming. You will learn basic computer programming concepts and terminologies such as variables, constants, operators, expressions, conditional statements, loops, and functions. This Python course includes hands-on exercises to help you understand the components of Python programming while incrementally developing more significant programs. The exercises in this course will be based on small assignments which will relate to real-world problems.No previous programming knowledge needed.
In this course, you will learn key methods for discovering how content can be divided into skills and concepts and how to measure student knowledge while it is changing – i.e. the student is learning.This course will also cover related methods for discovering structure in unlabeled data, such as factor analysis and clustering. It will also cover related methods for relationship mining including how to validly conduct correlation mining and how to automatically discover association rules and sequential rules.This mini-course does not assume prior programming knowledge beyond what you will already have learned in other courses in this MicroMasters, although advanced tools will be discussed for interested students.This course includes content also offered in the University of Pennsylvania’s edX MOOC, Big Data and Education, weeks 4, 5, and 7.
In this course, you will learn how relationships between people, artifacts, and ideas within learning settings can be analyzed and interpreted through social network analysis (SNA). You will learn how to prepare data and map these relationships to help you understand how people communicate and exchange information.The course will review foundational concepts and applications of social network analysis in learning analytics. You will also learn how to use igraph and statnet R packages to manipulate, analyze, and visualize network data.Week 1: Navigating the Language of NetworksIntroduction to networks including the basic concepts in social network analysis, i.e. nodes, edges, adjacency matrix, one and two-mode networks, node degree, connected components, average shortest path, diameter, preferential attachment, network centrality. The week will involve a hands-on task showing students how to calculate basic metrics in R.Week 2: Applying Network Analysis in Educational ResearchOverview of educational research and evidence produced using SNA applications, including differentiation between self-reported and digitally collected network data; ethical considerations; interpretation of basic metrics. The week’s task will include exploratory analysis of the selected dataset, and interpretation of results.Week 3: The Use of Network Analytic Techniques in Learning AnalyticsIntroduction to the analysis of socio-technical networks, and applications of network analytic techniques in LA, i.e. community detection, bipartite network analysis, network clustering, integration with text analysis. Presentation of community detection, information flow analysis, and statistical approaches in network analysis. The students will be expected to select one approach out of those presented, and implement it on one of the suggested datasets in R.
How can data-intensive research methods be used to create more equitable and effective learning environments? In this course, you will learn how data from digital learning environments and administrative data systems can be used to help better understand relevant learning environments, identify students in need of support, and assess changes made to learning environments.This course pays particular attention to the ways in which researchers and data scientists can transform raw data into features (i.e., variables or predictors) used in various machine learning algorithms. We will provide strategies for using prior research, knowledge from practice, and logic to create features, as well as build and evaluate machine learning models. The process of building features will be discussed within a broader data-intensive research workflow using R.