Bring learning to life with SMU’s online master’s in the learning sciences
At the intersection of cognitive science, technology, and education lies the dynamic curriculum of the SMU Master of Science in the Learning Sciences (MSLS) program. Designed to equip professionals with the tools and understanding to shape effective learning experiences in diverse environments, this 30-credit, fully online program caters to innovative educators, corporate trainers, instructional designers, and professionals who seek to deepen their expertise in data science, instructional design, and immersive learning technologies.
What can I do with a learning sciences degree?
The online M.S. in Learning Sciences equips students with the skills to design effective and innovative learning systems and analyze and manage complex learning data in a broad range of settings.
- Corporate training and consulting
- Instructional design
- Educational technology
- Online learning programs
- Museum exhibit design
- Library programming
Mastering the art and science of education
All online M.S. in the Learning Sciences students will navigate a comprehensive curriculum featuring two foundational courses complemented by a specialized 6-credit research methodologies course. Then you’ll choose a specialization in Learning & Technology Design, Learning Analytics, or build your own elective curriculum by combining 15 credits of courses from each specialization. Finally, all students will conclude their learning sciences degree with a capstone project that challenges them to use skills gained throughout the program to identify, research, and propose a solution to a contemporary learning science issue.
15 credits core curriculum
- Introduction to the Learning Sciences (3 credits)
- Theories and Trends in the Learning Sciences (3 credits)
- Research Methodologies in the Learning Sciences (6 credits)
Following completion of specialization courses:
- Applied LS Capstone Project (3 credits)
Learn to use data science and leading tech to enhance educational experiences across industries
Learning Analytics
- Introduction to Learning Analytics
- Data Modeling and the Learning Sciences
- Data, Education and Society
- Advanced Methods in Learning Analytics
Learning and Technology Design
- Immersive Learning Design
- Embodied Learning Design
- Video Games and Virtual Environments for Learning
- Understanding, Representing and Analyzing Human Experiences
Build your own specialization
Combine an assortment of Learning Analytics and Learning and Technology Design courses to complete 15 credit hours of elective courses. Note that some elective courses have prerequisites.
Online Master of Science in the Learning Sciences course descriptions
Core curriculum
Introduction to the Learning Sciences (3 credit hours)
Prerequisite: None
The purpose of this course is to understand different approaches to theorizing and studying learning and epistemology as represented by developments in educational research. This course attempts to synthesize the scientific basis of learning, including: (1) memory and the structure of knowledge; (2) problem solving and reasoning; (3) the early foundations of learning; (4) regulatory processes that govern learning, including metacognition; and (5) how symbolic thinking emerges from the culture and community of the learner. This course analyzes equity and ethical issues in the learning sciences, explores learning in formal and informal environments (museums, games, home, etc.) and discusses methodological issues of doing research in the learning sciences.
The purpose of this course is to understand different approaches to theorizing and studying learning and epistemology as represented by developments in educational research. This course attempts to synthesize the scientific basis of learning, including: (1) memory and the structure of knowledge; (2) problem solving and reasoning; (3) the early foundations of learning; (4) regulatory processes that govern learning, including metacognition; and (5) how symbolic thinking emerges from the culture and community of the learner. This course analyzes equity and ethical issues in the learning sciences, explores learning in formal and informal environments (museums, games, home, etc.) and discusses methodological issues of doing research in the learning sciences.
Theories and Trends in the Learning Sciences (3 credit hours)
Prerequisite: None
This survey course focuses on recent advances and current trends in cognitive science, learning sciences, education, and immersive learning technologies. It examines a range of theoretical and empirical models, equity and ethical issues, as well as design approaches to learning, focusing on the nature of cognition and the cognitive mechanisms underlying learning. To critically engage with these different theoretical approaches, each will be evaluated to see whether the proposed approach can provide interesting answers to the following focus questions related to education: 1) What are the cognitive and neural mechanisms that support the learning sciences? 2) How does the process of learning change human cognition and the brain, such that new discoveries and technologies can emerge from this change?
This survey course focuses on recent advances and current trends in cognitive science, learning sciences, education, and immersive learning technologies. It examines a range of theoretical and empirical models, equity and ethical issues, as well as design approaches to learning, focusing on the nature of cognition and the cognitive mechanisms underlying learning. To critically engage with these different theoretical approaches, each will be evaluated to see whether the proposed approach can provide interesting answers to the following focus questions related to education: 1) What are the cognitive and neural mechanisms that support the learning sciences? 2) How does the process of learning change human cognition and the brain, such that new discoveries and technologies can emerge from this change?
Research Methodologies in the Learning Sciences (6 credit hours)
Prerequisite: None
This course presents introductory research procedures in the social sciences, including the exploration of theoretical foundations and practical use of basic tools and programs needed for quantitative and qualitative data analysis. It examines how different methodologies can complement or compete with the other and showcases how quantitative and qualitative methods are applied in the field of learning sciences with particular emphasis on data about–and therefore issues in–learning environments such as classrooms, online courses, apprenticeships and internships, museum exhibits, after-school programs, and other formal and informal educational contexts.
This course presents introductory research procedures in the social sciences, including the exploration of theoretical foundations and practical use of basic tools and programs needed for quantitative and qualitative data analysis. It examines how different methodologies can complement or compete with the other and showcases how quantitative and qualitative methods are applied in the field of learning sciences with particular emphasis on data about–and therefore issues in–learning environments such as classrooms, online courses, apprenticeships and internships, museum exhibits, after-school programs, and other formal and informal educational contexts.
Applied LS Capstone Project (3 credit hours)
Prerequisite: Satisfactory completion of core courses
As part of the program requirements for the learning sciences (LS) master’s program, you will complete a Capstone Project. The Capstone Project demonstrates the depth and breadth of your educational growth through the program and highlights the knowledge and skills you have gained as well as your development as a reflective practitioner. The Applied LS Capstone Project provides you with the opportunity to apply, integrate and synthesize key concepts that you learn from courses in your program of study. The project requires that you identify an authentic and challenging learning sciences problem and then research, design and develop a proposed solution to meet that need. You should begin thinking about your Capstone Project during your first semester of study. A satisfactory project should address a local problem or issue related to learning. For that, students need to: design and develop a sophisticated needs analysis about addressing a situation/issue; lay out the theoretical foundations that they will apply based on the field of the learning sciences; and describe a solution in detail, including its constituent components and how it is informed by theory.
As part of the program requirements for the learning sciences (LS) master’s program, you will complete a Capstone Project. The Capstone Project demonstrates the depth and breadth of your educational growth through the program and highlights the knowledge and skills you have gained as well as your development as a reflective practitioner. The Applied LS Capstone Project provides you with the opportunity to apply, integrate and synthesize key concepts that you learn from courses in your program of study. The project requires that you identify an authentic and challenging learning sciences problem and then research, design and develop a proposed solution to meet that need. You should begin thinking about your Capstone Project during your first semester of study. A satisfactory project should address a local problem or issue related to learning. For that, students need to: design and develop a sophisticated needs analysis about addressing a situation/issue; lay out the theoretical foundations that they will apply based on the field of the learning sciences; and describe a solution in detail, including its constituent components and how it is informed by theory.
Learning Analytics (LA) specialization curriculum:
Introduction to Learning Analytics (3 credit hours)
Prerequisite: None
In this course, students will examine the application of data science as a means to better understand and improve learning. Specifically, students will think critically about the ways in which data scientists can support research and improvement in educational organizations of all types. Anchored in the fields of learning analytics (LA) and educational data mining (EDM), this course analyzes the unique opportunities and challenges associated with applying data science methods to data stemming from schools, universities, and a myriad of learning opportunities. The course will cover the history of learning analytics, typical data and methods used, the importance of measurement, and the implementation of learning analytics products.
In this course, students will examine the application of data science as a means to better understand and improve learning. Specifically, students will think critically about the ways in which data scientists can support research and improvement in educational organizations of all types. Anchored in the fields of learning analytics (LA) and educational data mining (EDM), this course analyzes the unique opportunities and challenges associated with applying data science methods to data stemming from schools, universities, and a myriad of learning opportunities. The course will cover the history of learning analytics, typical data and methods used, the importance of measurement, and the implementation of learning analytics products.
Data Modeling and the Learning Sciences (3 credit hours)
Prerequisite: Introduction to Learning Analytics
This course explores how new literacy and numeracy practices can be leveraged to support learners/learning with and without access to technology. It develops academic literacies for constructing and presenting oral and written arguments and delivery and critiquing explanations when using data and visualizations as evidence.
This course explores how new literacy and numeracy practices can be leveraged to support learners/learning with and without access to technology. It develops academic literacies for constructing and presenting oral and written arguments and delivery and critiquing explanations when using data and visualizations as evidence.
Data, Education and Society (3 credit hours)
Prerequisite: Introduction to Learning Analytics
This class will explore what learning analytics can do, what it has the potential to do for good, and what the potential is for harm. This course will discuss multiple uses and applications of analytics, where simple steps can mitigate risk, the relationship between validity and risk, and where risk mitigation will do more harm than good. This course will be executed in the context of real-world educational systems, challenges, problems, and with reference to original sources as much as possible.
This class will explore what learning analytics can do, what it has the potential to do for good, and what the potential is for harm. This course will discuss multiple uses and applications of analytics, where simple steps can mitigate risk, the relationship between validity and risk, and where risk mitigation will do more harm than good. This course will be executed in the context of real-world educational systems, challenges, problems, and with reference to original sources as much as possible.
Advanced Methods in Learning Analytics (3 credit hours)
Prerequisite: Research Methodologies or Instructor Permission
This course covers advanced methods in learning analytics with a focus on educational data mining (EDM). Students will learn how to execute these methods in standard software packages, and the limitations of existing implementations of these methods. Students will learn when and why to use these methods. Discussion of how these methods differ from more traditional statistical and psychometric approaches will be a key part of this course; in particular, students will study how many of the same statistical and mathematical approaches are used in different ways in learning analytics research communities.
This course covers advanced methods in learning analytics with a focus on educational data mining (EDM). Students will learn how to execute these methods in standard software packages, and the limitations of existing implementations of these methods. Students will learn when and why to use these methods. Discussion of how these methods differ from more traditional statistical and psychometric approaches will be a key part of this course; in particular, students will study how many of the same statistical and mathematical approaches are used in different ways in learning analytics research communities.
Learning and Technology Design (LTD) specialization curriculum:
Immersive Learning Design (3 credit hours)
Prerequisite: None
This course is a deep dive into the theory, principles, and practices of immersive learning. Immersive learning environments integrate advanced technologies such as augmented reality, virtual reality, and artificial intelligence to create engaging, interactive, and personalized learning experiences. This course provides a comprehensive understanding of immersive learning, exploring the theories that ground it, its design principles, pedagogical approaches, and ethical considerations. In doing so, it is hoped that the learner will not only be exposed to a variety of tools and resources, but will have a broader understanding of the landscape.
This course is a deep dive into the theory, principles, and practices of immersive learning. Immersive learning environments integrate advanced technologies such as augmented reality, virtual reality, and artificial intelligence to create engaging, interactive, and personalized learning experiences. This course provides a comprehensive understanding of immersive learning, exploring the theories that ground it, its design principles, pedagogical approaches, and ethical considerations. In doing so, it is hoped that the learner will not only be exposed to a variety of tools and resources, but will have a broader understanding of the landscape.
Embodied Learning Design (3 credit hours)
Prerequisite: None
This course introduces students to the literature of embodied cognition and its relevance to the Learning Sciences. It is intended to advance learning, instruction, and the design of educational technologies by helping students rethink the learner as an integrated system of mind, body, and environment. Body-based processes—direct physical, social, and environmental interactions—are constantly mediating intellectual performance, sensory stimulation, communication abilities, and other conditions of learning. This course will explore an evidence-based framework that articulates principles of grounded and embodied learning for design and its implications for curriculum, classroom instruction, and student formative and summative assessment. In addition, this course is designed to help students tap their own rich personal resources in their academic endeavors. It will inform students’ development of new pragmatic tools for the practice of education and educational research including lenses for interpreting data, emphases for the design of learning environments, and principles for effective instruction. This course will examine how human cognition is mediated and implemented through body and body-based resources such as physically grounded metaphor, object use, perception and action.
This course introduces students to the literature of embodied cognition and its relevance to the Learning Sciences. It is intended to advance learning, instruction, and the design of educational technologies by helping students rethink the learner as an integrated system of mind, body, and environment. Body-based processes—direct physical, social, and environmental interactions—are constantly mediating intellectual performance, sensory stimulation, communication abilities, and other conditions of learning. This course will explore an evidence-based framework that articulates principles of grounded and embodied learning for design and its implications for curriculum, classroom instruction, and student formative and summative assessment. In addition, this course is designed to help students tap their own rich personal resources in their academic endeavors. It will inform students’ development of new pragmatic tools for the practice of education and educational research including lenses for interpreting data, emphases for the design of learning environments, and principles for effective instruction. This course will examine how human cognition is mediated and implemented through body and body-based resources such as physically grounded metaphor, object use, perception and action.
Video Games and Virtual Environments for Learning (3 credit hours)
Prerequisite: None
This course investigates how, why, and when video games can foster learning on multiple levels. This course will explore the kinds of learning and social interactions video games encourage and develop critical thinking skills around the research on games and learning. Drawing on research from education, psychology, communication, and the growing field of games studies, this course will examine the history of video games, research on game play and players, review how researchers from different disciplines have conceptualized and investigated learning in playing and designing games, and what is known about possible outcomes. The course will also address issues of gender, race and violence that have been prominent in discussions about the impact of games.
This course investigates how, why, and when video games can foster learning on multiple levels. This course will explore the kinds of learning and social interactions video games encourage and develop critical thinking skills around the research on games and learning. Drawing on research from education, psychology, communication, and the growing field of games studies, this course will examine the history of video games, research on game play and players, review how researchers from different disciplines have conceptualized and investigated learning in playing and designing games, and what is known about possible outcomes. The course will also address issues of gender, race and violence that have been prominent in discussions about the impact of games.
Understanding, Representing and Analyzing Human Experiences (3 credit hours)
Prerequisite: Research Methodologies or Instructor Permission
This course explores responses to a core problem in the Learning Sciences: how do you do justice to the human experiences of learning and interaction that are so fundamental to efforts in research and practice? It follows techniques of elicitation and analysis that have been developed as historical responses to this question, and that are all current practices of researchers and practitioners in education-related fields. Underlying each of these techniques is a theory of learning and interaction; while these theories are not the focus of the course, they will be engaged to support students in considering how to deploy the techniques in coherent and principled ways. To participate in the course, students will engage with people and communities they have contact with, to practice the techniques of the course by implementing them. Problems and themes addressed in this course dovetail with the core course in Research Methods.
This course explores responses to a core problem in the Learning Sciences: how do you do justice to the human experiences of learning and interaction that are so fundamental to efforts in research and practice? It follows techniques of elicitation and analysis that have been developed as historical responses to this question, and that are all current practices of researchers and practitioners in education-related fields. Underlying each of these techniques is a theory of learning and interaction; while these theories are not the focus of the course, they will be engaged to support students in considering how to deploy the techniques in coherent and principled ways. To participate in the course, students will engage with people and communities they have contact with, to practice the techniques of the course by implementing them. Problems and themes addressed in this course dovetail with the core course in Research Methods.
Build your own specialization
Combine an assortment of Learning Analytics and Learning and Technology Design courses listed above to complete 15 credit hours of elective courses.
Earn your master’s in the learning sciences from experts and innovators
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Admissions Deadlines
Jul
15
Priority Deadline
July 15
Fall 2024
Aug
5
Final Deadline
August 5
Fall 2024
Aug
26
Class Start Date
August 26
Fall 2024