STEM for PK-12 Educators

User modeling is a cross-disciplinary research field that attempts to construct models of human behavior within a specific computer environment. Contrary to traditional artificial intelligence research, the goal is not to imitate human behavior as such, but to make the machine able to understand the expectations, goals, knowledge, information needs, and desires of a user in terms of a specific computing environment. The computer representation of this information about a user is called a user model, and systems that construct and utilize such models are called user modeling systems. A simple example of a user model would be an e-commerce site which makes use of the user’s and similar users’ purchasing and browsing behavior in order to better understand the user’s preferences. In this class, the focus is on obtaining a general understanding of user modeling, and an understanding of how to apply user modeling techniques. Students will read seminal papers in the user modeling literature, as well as complete a course project where students build a system that explicitly models the user.

This course will introduce students to graphical models, such as Bayesian networks, Hidden Markov Models, Kalman filters, particle filters, and structural equation models. Graphical models are applicable in a wide variety of work in computer science for reasoning under uncertainty such as user modeling, speech recognition, computer vision, object tracking, and determining a robot’s location. This course will cover 1) using data to estimate the parameters and structure of a model using techniques such as expectation maximization, 2) understanding techniques for performing efficient inference on new observations such as junction trees and sampling, and 3) learning about evaluation techniques to determine whether a particular model is a good one.

This course introduces students to techniques for performing rigorous empirical research in computer science. Since good empirical work depends on asking good research questions, this course will emphasize creating conceptual frameworks and using them to drive research. In addition to helping students understand what makes a good research question and why, some elementary statistics will be covered. Furthermore, students will use and implement computationally intensive techniques such as randomization, bootstrapping, and permutation tests. The course also covers experiments involving human subjects, and some of the statistical and non-statistical difficulties researchers often encounter while performing such work (e.g., IRB (Institutional Review Board), correlated trials, and small sample sizes). While this course is designed for students in Human Computer Interaction, Interactive Media & Game Development, and Learning Sciences and Technologies, it is appropriate for any student with programming experience who is doing empirical research.

Students will learn how to enable educational technology to adapt to the user and about typical architectures used by existing intelligent tutoring systems for adapting to users. Students will see applications of decision theoretic systems, reinforcement learning, Markov models for action selection, and Artificial Intelligence (AI) planning. Students will read papers that apply AI techniques for the purpose of adapting to users. Students will complete a project that applies these techniques to build an adaptive educational system.

This introductory online synchronous course surveys the landscape of PK-12 STEM education at the school, classroom, and learner levels. Students analyze research and STEM education frameworks to determine the critical elements of high-quality STEM related to teaching & learning, learner’s mindset, agency, and identity. Students consider their own teaching & learning experience, learning theories, and best practices as they synthesize a personalized framework of high-quality STEM education pertaining to their own educational setting. A special emphasis is given to the problem-solving process (otherwise known as the “engineering design process”), stewardship to community & earth, and classroom climate, as students consider the desired outcomes of integrated STEM pedagogy.

This online synchronous course addresses several elements of high-quality teaching, as well as laying the foundation for the variety of teaching & learning styles in different contexts (PreK-12 classrooms, out of school time settings, etc.). Students will discuss research related to family & community, culturally & linguistically sustaining practices, social & emotional learning, Universal Design for Learning, and engaging all learners in STEM. The course will also address different ways of knowing & learning and connecting STEM learning to learners’ culture and place. Students will develop concrete plans to apply course topics into their practice, aligned with professional standards.

This online synchronous course unpacks the elements of high-quality instructional materials (HQIMs), defined by the MA Department of Education and curricular materials exhibiting a coherent sequence of lessons that target learning of grade-appropriate and standards-aligned skills and knowledge through instructional strategies that are well supported by research and other characteristics such as engaging content and inclusive design. Students review, analyze, and identify high quality STEM curricula. Differences between STEM and project-based learning (PBL) curricular materials are discussed. Students review key literature on curriculum development through equity and justice frameworks (i.e. Understanding by Design, Science in the City). The final project includes the development/adaptation of a high-quality integrated STEM project aligned with State standards and practices, with key emphasis given to the process of creating standards-aligned learning targets.

In this online synchronous course, students unpack the elements of high-quality performance assessments that allow learners to demonstrate the knowledge and skills they have gained through a variety of methods. Approaches to grading and feedback to learners through an asset-based framework will be discussed. Students will analyze different types of rubrics and develop a standard-aligned rubric for a STEM project of their choice.

This online synchronous course explores the differences between formal and informal (out-of-school-time) PreK-12 education. Students will review the role of informal STEM education on learners’ motivation and aspiration towards STEM majors and careers, as well as the impact on learners’ mindset and skills. This course will also review how global STEM competitions inspire students and help them develop key collaboration and problem-solving skills. An experiential component of ‘STEM in the Community’ will be included in this course.

In this online synchronous course students will review several theories related to teamwork and collaboration and the translation of these theories into successful practices in STEM education classes and the school as a workplace. Theories and strategies for improving team dynamics will be introduced, and participants will learn what factors are shown to lead to greater innovation and creativity on teams. We will also discuss implicit bias and stereotyping on teams, and how to prevent and minimize their negative impacts on participants. Multiple strategies for team formation in the classroom will be discussed, and we will explore how to teach students teamwork skills during project-based classes. The course will include an opportunity to apply some of the theories and strategies for effective and equitable teamwork in the classroom and to reflect on the utility of different approaches.

This online synchronous course explores key topics in the forefront of STEM education research and practice. Course offerings will change regularly and may include topics such as integrated STEM through robotics, Storyline model of teaching and learning, and more. Students may earn credit for multiple offerings of this course provided each offering bears distinct course descriptions and course content.

A seminar will be developed and facilitated by the STEM Education Center’s members and/or adjunct faculty (with the STEM Education Center) who have extensive research and teaching experience in PreK-12 classrooms. This online synchronous course is intended for students working on their thesis/capstone/graduate qualifying project as part of the MS in Integrated STEM program and will be taken at the same time as their final project/thesis course. The seminar serves as an opportunity for graduate students to share their work and receive feedback and guidance from other students and the instructor. Students will report on the problem/research question, literature review, field-specific professional standards, community members and/or key stakeholders, solution/findings, and discussion.

Students enrolled in this online synchronous course complete an individual capstone project for the Integrated STEM program. This course serves as a practical integration of knowledge and skills. Students will define a STEM education related problem within their educational setting (e.g. own classroom, school, after school program), conduct a literature review related to the problem, draw on field-specific professional standards, engage with community members and/or key stakeholders, and propose a solution to the problem that weaves together theory and best-practices related to integrated STEM education. A public presentation is required.

This graduate qualifying project can be completed individually or in teams, is to be carried out in cooperation with a sponsor or external partner. It must be overseen by a faculty member affiliated with the Integrated STEM program. This offering integrates theory and practice of design for STEM education and should include the utilization of tools and techniques acquired in the program. In addition to a written report, this project must be presented in a formal public presentation to the program’s faculty and students.

The online synchronous thesis course consists of an individual research and development project (including action research) advised by a faculty member affiliated with the Program. A thesis proposal must be approved by the Integrated STEM program’s advisory committee and the student’s advisor, before the student can register for this course. The student must satisfactorily complete a written thesis document, and present the results to the advisor, program’s faculty, and students in a public presentation.

This course introduces students to probability, the mathematical description of random phenomena, and to statistics, the science of data. Students in this course will acquire the following knowledge and skills: • Probability models-mathematical models used to describe and predict random phenomena. Students will learn several basic probability models and their uses, and will obtain experience in modeling random phenomena. • Data analysis-the art/science of finding patterns in data and using those patterns to explain the process which produced the data. Students will be able to explore and draw conclusions about data using computational and graphical methods. The iterative nature of statistical exploration will be emphasized. • Statistical inference and modeling-the use of data sampled from a process and the probability model of that process to draw conclusions about the process. Students will attain proficiency in selecting, fitting and criticizing models, and in drawing inference from data. • Design of experiments and sampling studies - the proper way to design experiments and sampling studies so that statistically valid inferences can be drawn. Special attention will be given to the role of experiments and sampling studies in scientific investigation. Through lab and project work, students will obtain practical skills in designing and analyzing studies and experiments. Course topics will be motivated whenever possible by applications and reinforced by experimental and computer lab experiences. One in-depth project per semester involving design, data collection, and statistical or probabilistic analysis will serve to integrate and consolidate student skills and understanding. Students will be expected to learn and use a statistical computer package such as MINITAB.

Students will research and develop a mathematical topic or pedagogical technique. The project will typically lead to classroom implementation; however, a project involving mathematical research at an appropriate level of rigor will also be acceptable. Preparation will be completed in conjunction with at least one faculty member from the Mathematical Sciences Department and will include exhaustive research on the proposed topic. The course will result in a detailed proposal that will be presented to the MME Project Committee for approval; continuation with the project is contingent upon this approval.

Students will implement and carry out the project developed during the project preparation course. Periodic contact and/or observations will be made by the project advisor (see MME 592 Project Preparation) in order to provide feedback and to ensure completion of the proposed task. Data for the purpose of evaluation will be collected by the students throughout the term, when appropriate. If the project includes classroom implementation, the experiment will last for the duration of a semester.

Students will complete a detailed statistical analysis of any data collected during the project implementation using techniques from MME 524-525 Probability, Statistics, and Data Analysis. The final report will be a comprehensive review of the relevant literature, project description, project implementation, any statistical results and conclusions. Project reports will be subject to approval by the MME Project committee and all students will be required to present their project to the mathematical sciences faculty. Course completion is contingent upon approval of the report and satisfactory completion of the presentation.

This course covers readings that represent the foundation of the learning sciences, including: Foundations (Constructivism, Cognitive Apprenticeship, & Situated Learning); Approaches (Project-based Learning, Model-based reasoning, Cognitive Tutors); and Scaling up educational interventions. The goal of this course is for students to develop an understanding of the foundations and approaches to the Learning Sciences so that they can both critically read current literature, as well as build on it in their own research.

In this class, students will read and review both classic and critical current journal articles about learning technologies developed in the Learning Sciences. This course is designed to educate students on current technological approaches to curricular design, implementation, and research in the Learning Sciences.

This course covers research methods used in the Learning Sciences. Students will gain expertise and understanding of think-aloud studies, cognitive task analysis, quantitative and qualitative field observations, log file analysis, psychometric, cognitive, and machine-learning based modeling, the automated administration of measures by computer, and issues of validity, reliability, and statistical inference specific to these methods. Students will learn how and when to apply a variety of methods relevant to formative, performance, and summative assessment in both laboratory and field settings. Readings will be drawn primarily from original source materials (e.g. journal articles and academic book chapters), in combination with relevant textbook chapters.

This course covers three key types of constructs that significantly impact learning and performance in real-world settings, including but not limited to educational settings. Students will gain understanding of the main theoretical frameworks, and major empirical results, that relate individuals’ meta-cognition, motivation, and affect to real-world outcomes, both in educational settings and other areas of life. Students will learn how theories and findings in these domains can be concretely used to improve instruction and performance, and complete final projects that require applying research in these areas to real-world problems. Students will do critical readings on research on this topic.

User modeling is a cross-disciplinary research field that attempts to construct models of human behavior within a specific computer environment. Contrary to traditional artificial intelligence research, the goal is not to imitate human behavior as such, but to make the machine able to understand the expectations, goals, knowledge, information needs, and desires of a user in terms of a specific computing environment. The computer representation of this information about a user is called a user model, and systems that construct and utilize such models are called user modeling systems. A simple example of a user model would be an e-commerce site which makes use of the user’s and similar users’ purchasing and browsing behavior in order to better understand the user’s preferences. In this class, the focus is on obtaining a general understanding of user modeling, and an understanding of how to apply user modeling techniques. Students will read seminal papers in the user modeling literature, as well as complete a course project where students build a system that explicitly models the user.

This course will introduce students to graphical models, such as Bayesian networks, Hidden Markov Models, Kalman filters, particle filters, and structural equation models. Graphical models are applicable in a wide variety of work in computer science for reasoning under uncertainty such as user modeling, speech recognition, computer vision, object tracking, and determining a robot’s location. This course will cover 1) using data to estimate the parameters and structure of a model using techniques such as expectation maximization, 2) understanding techniques for performing efficient inference on new observations such as junction trees and sampling, and 3) learning about evaluation techniques to determine whether a particular model is a good one.

This course introduces students to techniques for performing rigorous empirical research in computer science. Since good empirical work depends on asking good research questions, this course will emphasize creating conceptual frameworks and using them to drive research. In addition to helping students understand what makes a good research question and why, some elementary statistics will be covered. Furthermore, students will use and implement computationally intensive techniques such as randomization, bootstrapping, and permutation tests. The course also covers experiments involving human subjects, and some of the statistical and non-statistical difficulties researchers often encounter while performing such work (e.g., IRB (Institutional Review Board), correlated trials, and small sample sizes). While this course is designed for students in Human Computer Interaction, Interactive Media & Game Development, and Learning Sciences and Technologies, it is appropriate for any student with programming experience who is doing empirical research.

Students will learn how to enable educational technology to adapt to the user and about typical architectures used by existing intelligent tutoring systems for adapting to users. Students will see applications of decision theoretic systems, reinforcement learning, Markov models for action selection, and Artificial Intelligence (AI) planning. Students will read papers that apply AI techniques for the purpose of adapting to users. Students will complete a project that applies these techniques to build an adaptive educational system.

This course introduces students to probability, the mathematical description of random phenomena, and to statistics, the science of data. Students in this course will acquire the following knowledge and skills: • Probability models-mathematical models used to describe and predict random phenomena. Students will learn several basic probability models and their uses, and will obtain experience in modeling random phenomena. • Data analysis-the art/science of finding patterns in data and using those patterns to explain the process which produced the data. Students will be able to explore and draw conclusions about data using computational and graphical methods. The iterative nature of statistical exploration will be emphasized. • Statistical inference and modeling-the use of data sampled from a process and the probability model of that process to draw conclusions about the process. Students will attain proficiency in selecting, fitting and criticizing models, and in drawing inference from data. • Design of experiments and sampling studies — the proper way to design experiments and sampling studies so that statistically valid inferences can be drawn. Special attention will be given to the role of experiments and sampling studies in scientific investigation. Through lab and project work, students will obtain practical skills in designing and analyzing studies and experiments. Course topics will be motivated whenever possible by applications and reinforced by experimental and computer lab experiences. One in-depth project per semester involving design, data collection, and statistical or probabilistic analysis will serve to integrate and consolidate student skills and understanding. Students will be expected to learn and use a statistical computer package such as MINITAB.

This course covers readings that represent the foundation of the learning sciences, including: Foundations (Constructivism, Cognitive Apprenticeship, & Situated Learning); Approaches (Project-based Learning, Model-based reasoning, Cognitive Tutors); and Scaling up educational interventions. The goal of this course is for students to develop an understanding of the foundations and approaches to the Learning Sciences so that they can both critically read current literature, as well as build on it in their own research.

In this class, students will read and review both classic and critical current journal articles about learning technologies developed in the Learning Sciences. This course is designed to educate students on current technological approaches to curricular design, implementation, and research in the Learning Sciences.

This course covers research methods used in the Learning Sciences. Students will gain expertise and understanding of think-aloud studies, cognitive task analysis, quantitative and qualitative field observations, log file analysis, psychometric, cognitive, and machine-learning based modeling, the automated administration of measures by computer, and issues of validity, reliability, and statistical inference specific to these methods. Students will learn how and when to apply a variety of methods relevant to formative, performance, and summative assessment in both laboratory and field settings. Readings will be drawn primarily from original source materials (e.g. journal articles and academic book chapters), in combination with relevant textbook chapters.

This course covers three key types of constructs that significantly impact learning and performance in real-world settings, including but not limited to educational settings. Students will gain understanding of the main theoretical frameworks, and major empirical results, that relate individuals’ meta-cognition, motivation, and affect to real-world outcomes, both in educational settings and other areas of life. Students will learn how theories and findings in these domains can be concretely used to improve instruction and performance, and complete final projects that require applying research in these areas to real-world problems. Students will do critical readings on research on this topic.

This course is about the theory and the practice of formative assessment. The practice will involve bringing those theories to life in the classroom. Participants will be required to actively implement the formative assessment cycle in their own teaching. Online tools that facilitate the formative assessment process will be used by the teachers. One such tool that will be required is ASSISTments. Participants will decide what data to collect evaluate and analyze. They will analyze the data in this class and with their students. They will examine their own instruction by videotaping themselves and sharing their experiences with the group. Participants will go through these steps repeatedly during the course. Participants will be required to synthesize and critique course materials through written documents and formal and informal presentations.