Student Achievement

Bachelor’s Degrees of the Minerva Schools at KGI

The Minerva Schools at KGI (MSKGI) focuses on the success of our students. We provide active learning in an all-seminar program because this form of pedagogy has been shown to be superior to traditional lecture-based instruction. Our students learn to work as individuals and as members of teams, and learn habits of mind and foundational concepts that will serve them in good stead for a lifetime. We also support co-curricular and extracurricular opportunities and provide robust student support services to assist students through challenges they may face while enrolled in our programs.

Student Demographics

One of the key characteristics of MSKGI is its global character. Students come to Minerva from all over the world to study and live in seven major world cities during the four-year undergraduate program. All undergraduate students are full-time.

We have 631 students for the 2019-2020 Academic Year. The first-year students live all year in San Francisco; second-year students spend fall semester in Seoul and spring semester in Hyderabad; third-year students spend their fall semester in Berlin and spring semester in Buenos Aires; and fourth-year students spend their fall semester in London and spring semester in Taipei, before returning to San Francisco for their last month of classes before graduation. They come from approximately 60 different countries, with no single country dominating the enrollment. They are approximately half men and half women.

Enrollment and Retention

Minerva's first class of 28 students matriculated in 2014. After one year of coursework, they took a gap year and then joined, as sophomores, a larger group of 111 students who had matriculated in 2015. Together, this group comprised the first class of Minerva bachelor's graduates, completing their degrees in May 2019.

FY20 Undergraduate Retention by Cohort

Student Learning

Degree Learning Objectives

Minerva expects all undergraduate students to meet the following Degree Learning Objectives (DLOs.) These DLOs are introduced in the first-year Cornerstone courses and consist of “habits of mind” and “foundational concepts” (HCs,) which are threaded and assessed throughout the four years of study. A habit of mind is a mental skill that comes to be triggered automatically with practice; a foundational concept is fundamental knowledge that is broadly applicable, which typically does require deliberation. To be included, the habit of mind or foundational concept must be derived from an aspect of one of the four core competencies noted below:

  1. lead students to be able to do something useful in ordinary life after graduation (specialized knowledge comes later in the curriculum);
  2. be broadly applicable, as indicated by the fact that it is used in courses offered in at least two of Minerva’s colleges;
  3. be justified either by empirical findings, proofs, or well-established best practices — particularly those that support functioning ethically in a global context; and
  4. lead to specific behaviors that can be evaluated with rubrics; the HC cannot be so general or vague that it cannot be systematically and reliably evaluated.

Each of the following four core competencies has been broken down into more specific aspects, and each of these aspects in turn includes a set of habits of mind and foundational concepts.

Thinking Critically

Evaluating Claims

  • Identify how prior experiences and expectations affect inferences drawn from different forms of communication, and react accordingly.
  • Situate a work in its relevant context (e.g., historical, disciplinary, cultural).
  • Actively and critically engage with texts and other forms of communication.
  • Evaluate whether hypotheses are based on plausible premises or assumptions.
  • Evaluate whether hypotheses lead to testable predictions.
  • Use estimation and approximation techniques appropriately.

Evaluating justifications

  • Identify and appropriately structure the information needed to support an argument effectively.
  • Distinguish between categories and types of information to determine source quality.
  • Analyze and apply deductive reasoning.
  • Analyze and apply inductive reasoning.
  • Identify and correct logical fallacies.

Analyzing data

  • Calculate and interpret descriptive statistics appropriately.
  • Apply and interpret fundamental concepts of probability, including conditional and bayesian probabilities.
  • Identify different types of distributions and make inferences based on samples from distributions appropriately.
  • Apply and interpret confidence intervals.
  • Apply and interpret measures of correlation; distinguish correlation and causation.
  • Apply and interpret regression.
  • Apply, interpret, and distinguish practical and statistical significance.

Analyzing decisions

  • Analyze the relations among interacting motivating factors that shape behavior.
  • Identify and evaluate underlying goals and the values on which they are based, as well as the guiding principles that determine how an individual or group will try to attain these goals.
  • Consider different types of costs and benefits for all stakeholders.
  • Identify and explain how biases result from psychological mechanisms or use of heuristics.
  • Identify methods to mitigate the effect of biases and determine when it is appropriate to do so. 
  • Calculate expected utilities to analyze decisions in the presence of risk.
  • Apply and interpret decision trees to explore the consequences of alternative choices.

Analyzing problems

  • Characterize the nature of the problem.
  • Organize problems into tractable components and design solutions.
  • Identify and evaluate whether there are suitable existing solutions to a problem or whether a creative new solution is required. 
  • Identify and classify the relevant variables of system, problem, or model.
  • Apply and evaluate game-theoretic models.

Thinking Creatively

Facilitating discovery

  • Evaluate the link between hypothesis-driven research and the theories or observations that motivate it.
  • Interpret, analyze, and create data visualizations.
  • Recognize how models can be used to explain a set of data and generate new predictions.

Applying research methods

  • Design and interpret observational studies.
  • Design and interpret experimental studies.
  • Design and interpret case studies.
  • Design and interpret primary research performed as interviews or surveys (individually or in groups).
  • Evaluate and incorporate replicability in empirical study designs.
  • Identify and evaluate appropriate control groups for empirical study designs.
  • Design effective sampling methods and evaluate the interpretation of results accordingly.

Solving problems

  • Evaluate and use effective strategies to learn or teach specific types of material.
  • Use analogies in problem solving appropriately.
  • Identify and apply constraint satisfaction as a way to solve problems.
  • Identify when to use heuristics and when to avoid them.
  • Apply algorithmic thinking strategies to solve problems and effectively implement working code.
  • Evaluate and apply optimization techniques appropriately.
  • Apply iterative design thinking to conceive and refine products or solutions.

Communicating Effectively

Using language

  • Follow established guidelines to present yourself and your work products professionally.
  • Formulate a well-defined thesis.
  • Effectively organize communications.
  • Communicate with a clear and precise style.
  • Understand and use connotations, tone, and style.
  • Tailor oral and written work by considering the situation and perspective of the people receiving it.

Using nonverbal communication

  • Describe, analyze, and organize characteristics of communicative and expressive mediums at the level of form and structure.
  • Interpret, evaluate, and utilize nonverbal communication. 
  • Apply principles of perception and cognition in oral and multimedia presentations and in design.
  • Identify, analyze, and organize characteristics to infer possible meanings in multimedia work.

Interacting Effectively

Interacting with complex systems

  • Analyze and apply decompositions of complex systems into constituent parts.
  • Describe interactions among events or characteristics of a system at different levels of analysis to generate explanations of phenomena.
  • Identify emergent properties of complex systems and discern their causes.
  • Identify ways that multiple causes interact to produce complex effects.
  • Analyze how network structure affects interactions within a network.
  • Recognize the role of attractors and sensitivity to varying conditions in the behavior of complex systems.

Negotiating and persuading

  • Use a structured approach to negotiation to reach desired objectives.
  • Use choice architecture to influence other people’s decisions.
  • Analyze how incentives, reinforcement, and punishment alter behavior and utilize them appropriately.
  • Recognize and use appropriate cognitive tools to persuade.
  • Recognize and use appropriate emotional tools of persuasion.
  • Present views and work with an appropriate level of confidence.

Working with others

  • Apply principles of effective leadership.
  • Recognize how to influence group interactions by exerting different types of power.
  • Recognize the strengths and weaknesses of a position, and develop a plan to achieve your goals accordingly.
  • Identify and utilize people's different skills, abilities, traits, attitudes, and beliefs.
  • Identify and mitigate conformity in group settings.
  • Identify and monitor your strengths and weaknesses; mitigate behaviors and habits that impair effective performance.
  • Use emotional intelligence to interact effectively.
  • Follow through on commitments, be proactive, and take responsibility.

Resolving ethical problems

  • Identify ethical problems, framing them in a way that helps to resolve them.
  • Resolve conflicts among competing ethical claims and act in a way that best satisfies relevant ethical considerations.

Program Learning Objectives

In addition to the Degree Learning Objectives, students are expected to master field-specific Program Learning Objectives.

College of Arts and Humanities

  • Understand and apply levels of analysis in Arts and Humanities
  • Master multimodal communications
  • Understand and apply principles of human creativity and emotional expression
  • Understand and apply principles of philosophy and ethics
  • Understand and apply principles and practices of historical analysis

College of Computational Sciences

  • Master inductive and deductive reasoning
  • Design, develop and use formal and computational models to solve problems
  • Evaluate and analyze data and information
  • Design, develop and use decision-support tools
  • Think algorithmically and create algorithms to solve problems
  • Understand and apply essential descriptive and inferential statistics

College of Natural Sciences

  • Analyze scientific claims
  • Characterize the predictive value of a scientific claim
  • Grasp and utilize methods of ideation for problems requiring natural science expertise
  • Utilize natural sciences in design of technological solutions
  • Understand and apply research design and the scientific method

College of Social Sciences

  • Understand and apply levels of analysis in the Social Sciences
  • Master complex systems analysis
  • Understand and apply principles of individual human behavior
  • Understand and apply principles of human group behavior
  • Understand and apply principles and practices of cultural variation, contemporary and historical

College of Business

  • Evaluate, analyze and model data and information
  • Design, develop and use decision-making tools
  • Understand and engage in effective group dynamics
  • Understand and apply the essential concepts underlying finance
  • Understand and apply the essential concepts underlying marketing

Results of Assessments

MSKGI uses both internal and external assessments to measure student learning. 

Analyses of the Core Competencies

The habits of mind and foundational concepts derived from Minerva’s core competencies are introduced in the first-year Cornerstone courses. These outcomes continue to be assessed over the next three years in every Minerva course. This results in continuous evaluation of student performance on MSKGI’s undergraduate degree learning outcomes. Outcome data are analyzed at the end of each year. The Benchmark is set at a benchmark of 3.0 (Knowledge) on the rubric scale of 1-5.

FY20 Core Competencies Benchmarks

As students continue to be directly assessed on the habits of mind and foundation concepts introduced in the first-year curriculum in later courses, they receive feedback on their ability to transfer these outcomes to their upper-division coursework. 

Class of 2019 Successful Transfer Rate of Learning to New Contexts

Collegiate Learning Assessment + (CLA+)

Minerva administered the CLA+ test to provide an outside assessment of the effectiveness of the first-year curriculum. The assessment was given at the beginning and end of the fall and spring terms to first-year students and at graduation. The Council for Aid to Education administers the CLA+ each year to more than 20,000 undergraduate students attending approximately 100 different colleges and universities. More information about the CLA+ is available here. The table below gives the percentiles that Minerva students achieved when compared with freshmen and seniors at other institutions.

FY20 CLA+ Percentiles

Student Engagement

Minerva administers the State of Minerva (SOM) survey at the end of each academic semester to collect self-reported data from students. The Spring 2019 survey, plus other tracking measures, covered the Classes of 2019 through 2022 and provided the following data on student self-reported engagement and learning. 

  • 94% of seniors and 91% of juniors reported completing at least one external internship.
  • 90% of first-year students collaborated on a team-based project with students from different backgrounds. 
  • 75% of graduates reported having a full-time job or graduate school offers within four months of graduation.
  • 73% of all students report that they applied the skills they learned in the classroom to real-world contexts.
  • 73% gained a worldview they had not previously considered; 69% reported increased self-awareness of their identity and values.
  • 63% of first-year students felt empowered to pursue and realize their ideas in the city where they lived during the academic year; 43% of all students were actively involved with a local organization or community.
  • 48% explored career paths they had not previously considered. 

Master of Science in Decision Analysis (MDA)

Master of Science in Decision Analysis (MDA)

This 21-month, part-time graduate program is designed to impart key professional skills, with an emphasis on research, analysis, and practical decision-making. Students learn how to interpret complex data, find rational conclusions, devise potential solutions, and evaluate the implications of their choices.

Enrollment and Retention

The first pilot cohort of master's students participated in a 12-month full-time program and matriculated in September 2017. Upon their graduation, the program was redesigned as a 21-month part-time program and relaunched one year later. Master's students may extend their thesis work for up to one year from the expected graduation date.

FY20 MDA Retention by Cohort

Degree Learning Outcomes

The following learning outcomes are associated with the MDA degree:

  • Framing problems: Identify and research important real-world problems, learning the broad and integrative knowledge relevant to a particular research question or decision.
  • Contextualizing research: Critique and extend existing research, and articulate a deep understanding of the nature and complexity of the problems seen from multiple perspectives.
  • Synthesizing approaches: Apply multiple approaches, theories, and methods of analysis to formulate possible solutions to problems, integrating quantitative and qualitative methods, including computer-based data science.
  • Assessing solutions: Use all available information and appropriate analytical tools to assess and select the most effective solutions to problems, incorporating risk, ethical implications, and competing interests.
  • Measuring efficiency: Design processes to implement decisions effectively and measure the efficacy of planned objectives.

The Benchmark is set at a benchmark of 3.0 (Knowledge) on the rubric scale of 1-5 for the learning outcomes across the eight courses taken.

FY20 MDA Degree Learning Benchmarks

Post-Degree Activities

MDA graduates work in a wide variety of sectors, including but not limited to science, education, government, technology, and finance. Their roles are similarly diverse, including founders (29%), chief executives (18%), and managerial/directorial roles (29%).

FY20 MDA Degree Learning Outcome Benchmarks