Interdisciplinary Programs

Undergraduate Courses

CP 110X. PREPARING FOR CO-OPS

The WPI Cooperative Education Program (known as Co-op) provides an opportunity for students to alternate time in the classroom with extended periods of paid, full-time, career related work experience in industry or government agencies. This course is open to all students who are interested in participating in the Co-op program, spring or fall. Students will learn about the Co-op program, how to fit a Co-op into their schedule, how to apply to opportunities, job search skills such as resume writing, interviewing and networking, attend information panels with past Co-op participants, conduct informational interviews with employers and learn the impact Co-op has on their experience at WPI, such as financial aid and housing contracts.

ES 1020. Introduction to Engineering

Cat I (offered at least 1x per Year).
This course is for first year students with an interest in engineering. The course focuses on the design process. Students are introduced to engineering through case studies and reverse engineering activities. Students will learn the steps in the design process and how engineers use this process to create new devices. Teams of students are then assigned a design project that culminates in building and evaluating a prototype of their design. Results of the design project are presented in both oral and written reports. This course does not require any prior engineering background. Note: This course can be used towards the Engineering Science and Design distribution requirement in IE and ME.

ES 1310. Introduction to Computer Aided Design

Cat I (offered at least 1x per Year).
This introduction course in engineering graphical communications and design provides a solid background for all engineering disciplines. The ability to visualize, create and apply proper design intent and industry standards for simple parts, assemblies and drawings is a necessity for anyone in a technology environment. Computer Aided Design software is used as a tool to create 2D & 3D sketches, 3D parts, 3D assemblies and 2D drawings per an industry standard. Multiview and pictorial graphics techniques are integrated with ANSI standards for dimensioning and tolerances, sectioning, and generating detailed engineering drawings. Emphasis is placed on relating drawings to the required manufacturing processes. The design process and aids to creativity are combined with graphics procedures to incorporate functional design requirements in the geometric model. No prior engineering graphics or software knowledge is assumed.

ES 1500. Fundamentals of Systems Thinking

Cat I (offered at least 1x per Year).
Systems Thinking is a holistic approach to problem solving that recognizes that system behavior and performance are the result of underlying structures. Systems Thinking provides tools that enable program managers, systems engineers, scientists, economists, and business managers to identify, understand, and control systems in order to improve system performance. The Systems Thinking analysis accounts for feedback and resistance to change often exhibited by real world systems. In this course, students will study system identification and delineation, causal loops and feedback diagrams, stock-and-flow diagrams, system leverage points, delays and oscillations, mental models and unintended consequences, and behavior patterns; and use these concepts to improve the performance of engineering, business, and complex social systems. The course will explore great system failures, how they might have been avoided, and how we can learn from them. Finally, students will learn how Systems Thinking explains the occasional irrational behavior of individuals, departments, businesses, and governments. Examples covered in this course may include the failure of strictly technological fixes to social issues (as in the governments installation of wells in Togo in the 1980s,) the 2008 financial meltdown, the failure of the Lockheed L-188 Electra Turboprop Airplane, the failure of the Tacoma Narrows Bridge (Galloping Gertie) in 1940, the decline of many commercial fisheries around the world, the failure and success of companies like Research In Motion and Apple, and the unintended consequences of combating drug-related crime.

ES 2001. Introduction to Materials Science

Cat I (offered at least 1x per Year).
This beginning course provides important background for all science and engineering disciplines regarding the capabilities and limitations of materials in our everyday lives. Students are introduced to the fundamental theme of materials science structure-property-processing relationshipsin metals, ceramics, and plastics. Aspects of material structure range from the atomic to microstructural and macroscopic scales. In turn, these structural features determine the properties of materials. In particular, this course investigates connections between structure and mechanical properties, and how working and thermal treatments may transform structure and thus alter material properties. This knowledge is then applied to material selection decisions.

ES 2501. Introduction to Static Systems

Cat I (offered at least 1x per Year).
This is an introductory course in the engineering mechanics sequence that serves as a foundation for other courses in mechanical engineering. The course covers general two- and three-dimensional force and couple systems, distributed loads, resultant forces, moments of forces, free body diagrams, equilibrium of particles and finite sized bodies. Specific topics include friction, trusses, shear forces, bodies subjected to distributed loads, bending moments in beams, and first and second moments of plane areas.

ES 2502. Stress Analysis

Cat I (offered at least 1x per Year).
This is an introductory course that addresses the analysis of basic mechanical and structural elements. Topics include general concepts of stresses, strains, and material properties of common engineering materials. Also covered are two-dimensional stress transformations, principal stresses, Mohrs circle and deformations due to mechanical and thermal effects. Applications are to uniaxially loaded bars, circular shafts under torsion, bending and shearing and deflection of beams, and buckling of columns. Both statically determinate and indeterminate problems are analyzed.

ES 2503. Introduction to Dynamic Systems

Cat I (offered at least 1x per Year).
Engineers should be able to formulate and solve problems that involve forces that act on bodies which are moving. This course deals with the kinematics and dynamics of particles and rigid bodies which move in a plane. Topics covered will include: kinematics of particles and rigid bodies, equations of motion, work-energy methods, and impulse and momentum. In this course a basic introduction to mechanical vibration is also discussed. Basic equations will be developed with respect to translating and rotating coordinate systems.

ES 2800. Environmental Impacts of Engineering Decisions

Cat II (offered at least every other Year).
Engineering decisions can affect the environment on local and global scales. This course will introduce students to concepts that will make them aware of the ramifications of their engineering decisions, and is intended for engineering students of all disciplines. Specific topics the course will cover include: environmental issues, waste minimization, energy conservation, water conservation and reuse, regulations (OSHA, TSCA, RCRA, etc.), lifecycle assessment, risk assessment, sustainability, design for the environment, and environmental impact statements. Energy and mass balances will be applied to activities that impact the environment. Instruction will be provided through lectures, practitioner seminars, and a term project. Intended audience: all engineering majors desiring a general knowledge of the environmental impacts of engineering decisions. This course will be offered in 2022-23, and in alternating years thereafter.

ES 3001. Introduction to Thermodynamics

Cat I (offered at least 1x per Year).
This course emphasizes system and control volume modeling using conservation of mass and the First and Second Laws of Thermodynamics. Topics include an introduction to heat, work, energy, and power, properties of simple substances, and cycle analysis for power production and refrigeration.

ES 3002. Mass Transfer

Cat I (offered at least 1x per Year).
This course introduces the student to the phenomena of diffusion and mass transfer. These occur in processes during which a change in chemical composition of one or more phases occurs. Diffusion and mass transfer can take place in living systems, in the environment, and in chemical processes. This course will show how to handle quantitative calculations involving diffusion and/or mass transfer, including design of process equipment. Topics may include: fundamentals of diffusional transport, diffusion in thin films; unsteady diffusion; diffusion in solids; convective mass transfer; dispersion; transport in membranes; diffusion with chemical reaction; simultaneous heat and mass transfer; selected mass transfer operations such as absorption, drying, humidification, extraction, crystallization, adsorption, etc.

ES 3003. Heat Transfer

Cat I (offered at least 1x per Year).
This course presents the fundamentals of heat transfer in the three modes of conduction, convection, and radiation. Topics include steady-state and transient heat conduction, forced external and internal convection, natural convection, heat exchanger analysis, radiation properties, and radiative exchange between surfaces.

ES 3004. Fluid Mechanics

Cat I (offered at least 1x per Year).
A study of the fundamental laws of statics, kinematics and dynamics applied to fluid mechanics. The course will include fluid properties, conservation of mass, momentum and energy as applied to real and ideal fluids. Laminar and turbulent flows, fluid resistance and basic boundary layer theory will also be considered.

ES 3011. Control Engineering I

Cat I (offered at least 1x per Year).
Characteristics of control systems. Mathematical representation of control components and systems. Laplace transforms, transfer functions, block and signal flow diagrams. Transient response analysis. Introduction to the root-locus method and stability analysis. Frequency response techniques including Bode, polar, and Nichols plots. This sequence of courses in the field of control engineering (ES 3011) is generally available to all juniors and seniors regardless of department. A good background in mathematics is required; familiarity with Laplace transforms, complex variables and matrices is desirable but not mandatory. All students taking Control Engineering I should have an understanding of ordinary differential equations (MA 2051 or equivalent) and basic physics through electricity and magnetism (PH 1120/1121). Control Engineering I may be considered a terminal course, or it may be the first course for those students wishing to do extensive work in this field. Students taking the sequence of two courses will be prepared for graduate work in the field. Students may not receive credit for both ES 3011 and ECE 3012.

ES 3323. Advanced Computer Aided Design

Cat I (offered at least 1x per Year).
This course is intended to strengthen solid modeling and analysis skills with an emphasis on robust modeling strategies that capture design intent. The use of solid models for applications in mechanical design and engineering analysis is emphasized. Topics include: advanced feature-based modeling, variational design, physical properties, assembly modeling, mechanisms, and other analytical methods in engineering design.

ES 3501. A Project-Based Introduction to Systems Engineering

Cat I (offered at least 1x per Year).
Systems Engineering is a multifaceted discipline, involving human, organizational, and various technical variables that work together to create complex systems. This course is an introduction and overview of the methods and disciplines that systems engineers use to define and develop systems, with a particular focus on capstone projects. The course will include specific integrated examples, projects, and team building exercises to aid in understanding and appreciating fundamental principles. Topics covered will include: Introduction to Systems Engineering; Requirements Development; Functional Analysis; System Design; Integration, Verification and Validation; Trade Studies and Metrics; Modeling and Simulation; Risk Management; and Technical Planning and Management.

FY 160X. HUMANTRN ENGIN: PAST & PRESENT

In FY 160X, students confront a historically particular engineering challenge through role-play. For C 2019, our scenario is 19th-century Worcester, MA. Stepping into a different historical and cultural context, students encounter a variety of perspectives within a complex social environment to understand and address a historically specific engineering challenge, such as Worcester’s 19th-century waste management problems. They learn concepts, methods and skills from a variety of disciplines (in engineering and the humanities) while developing creative confidence to identify opportunities and apply knowledge to improve people’s lives and mitigate damage to the planet.

ID 200X. MAPPING YOUR MISSION

Every student that graduates from WPI has a major, but what about a mission? This course helps participants explore their personal values, strengths, and talents and the ways they can use these personal characteristics to improve the world around them. Through the course, participants will identify a personal mission and a plan to work toward achieving their mission. Participants will explore the ways their major and their mission can intersect. Suggested background: FY1800.

Graduate Courses

CS 565. User Modeling

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 users and similar users purchasing and browsing behavior in order to better understand the users 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.

CS 566. Graphical Models for Reasoning Under Uncertainty

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 robots 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.

CS 567. Empirical Methods for Human-Centered Computing

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.

CS 568. Artificial Intelligence for Adaptive Educational Technology

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.

ID 500. Responsible Conduct Of Research

The purpose of this zero credit course is to familiarize pre-doctoral and postdoctoral trainees with basic ethical issues in research confronting scientists and engineers. The course includes five lectures and five student-led discussion sessions on topics such as experimental design best practices, research involving animal subjects, authorship, and research misconduct. Student learning will be assessed through in-class formative assessments as well as small group presentations during the discussion sessions. The course is recommended for all graduate students and postdocs who are engaged in research.

ID 510. Undergraduate Research Mentoring

The purpose of this zero-credit course is to improve studentresearch mentoring proficiency forpre-doctoral and postdoctoral trainees. The course includes interactive, seminar style sessions on topics such as establishing expectations, maintaining effective communication, assessing understanding, fostering independence, using inclusive practicesdealing with ethics and mentoring groups of students. The seminar emphasizes experiential learning and the integration of knowledge drawn from reflection, discussion, readings and seminar activities with practice. The seminar is graded based upon attendance, doing the assignments, and participating in the activities.

ID 527. Fundamentals of Scientific Teaching and Pedagogy

The purpose of this zero credit course is to bolster teaching proficiency for pre-doctoral and postdoctoral trainees through in depth and interactive sessions on the science behind student learning, scientific teaching, assessments and rubrics, active learning, project based learning, inclusive learning environments, teaching philosophies, technology in the classroom, and course design. Participants will learn through both lecture and practicum sessions each week, and will work in small groups to develop a short teachable unit incorporating the techniques learned throughout the course, which they will ultimately present at the conclusion of the series. Students will also develop a statement of teaching philosophy during the course and receive feedback on the statement. The course is recommended for all graduate students and postdocs who are pursuing careers that will entail teaching in higher education as well as those interested in learning the fundamentals of pedagogy and effective teaching strategies. The course is offered annually each Fall.

MME 524-25. Probability, Statistics and Data Analysis I, II

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.

MME 592. Project Preparation (Part of a 3-Course Sequence with Mme 594 and Mme 596)

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.

MME 594. Project Implementation

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.

MME 596. Project Analysis and Report

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.

PSY 501. Foundations of the Learning Sciences

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.

PSY 502. Learning Environments in Education

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.

PSY 503. Research Methods for 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.

PSY 504. Meta-Cognition, Motivation, and Affect

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.

SEME 501. Foundations of the Learning Sciences

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.

SEME 502. Educational Learning Environments

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.

SEME 503. Research Methods for 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.

SEME 504. Meta-Cognition, Motivation, and Affect

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.

SEME 524-25. Probability, Statistics and Data Analysis I, II

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.

SEME 562. Issues in Education

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.

SEME 565. User Modeling

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 users and similar users purchasing and browsing behavior in order to better understand the users 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.

SEME 566. Graphical Models for Reasoning Under Uncertainty

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 robots 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.

SEME 567. Empirical Methods for Human-Centered Computing

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.

SEME 568. Artificial Intelligence for Adaptive Educational Technology

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.

IDG 598. Systems Engineering Leadership Project

This project-based course is an interdisciplinary exercise that integrates the technical aspects of systems engineering with the challenges of meeting business goals within the framework of the organizational structure. It allows students to apply the skills and knowledge acquired throughout the Systems Engineering Leadership curriculum. Students are encouraged to select projects with practical significance to their current and future professional responsibilities. Each project is normally conducted in teams of two to four students. They are administered, advised, and evaluated by WPI faculty as part of the learning experience, but students are also encouraged to seek mentorship from experienced systems engineers.

IDG 599. Capstone Project Experience in Power Systems Management

This project-based course is an interdisciplinary exercise that integrates the technical aspects of power systems engineering with challenges of meeting business goals within the framework of the corporate organizational structure. It allows the students to apply the skills and knowledge acquired throughout the Power Systems Management curriculum. Students are encouraged to select projects with practical significance to their current and future professional responsibilities. Each project is normally conducted in teams of two to four students. They are administered, advised, and evaluated by WPI faculty as part of the learning experience, but students are also encouraged to seek mentorship from experienced colleagues in the Power Systems profession.