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title: bok-ai-lab-20250411-glossary

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# bok-ai-lab-20250411-glossary

# AI Lab Glossary

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## Formative Assessment

Formative assessment refers to low-stakes evaluations conducted during the learning process to monitor student understanding and provide ongoing feedback for improvement​ (*cmu.edu*). The primary goal is to identify strengths and weaknesses in real time so instructors can adjust their teaching and students can adjust their learning strategies​ (*cmu.edu*).

In practice, formative assessments might include ungraded quizzes, one-minute papers, drafts, or in-class polling that help students gauge their progress. For example, a professor might give a short quiz or ask students to draw a concept map mid-lecture to see if they grasp the concept, then use the results to clarify misunderstandings. These assessments are typically low stakes (little or no point value) and emphasize feedback over grading, fostering a supportive learning environment where mistakes are viewed as learning opportunities.

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## **Summative Assessment**

Summative assessment is the high-stakes evaluation of student learning at the end of an instructional period, such as the conclusion of a unit, course, or semester​ (*cmu.edu*). It is designed to measure the extent to which learning outcomes or standards have been met by comparing performance against a benchmark or criterion​ (*cmu.edu*).

Common summative assessments in higher education include midterm and final exams, final projects, research papers, or capstone presentations. These assessments usually carry significant point values (high stakes) and often determine a large portion of the course grade. While summative assessments are evaluative, they can also inform future instruction; for instance, analyzing exam results can help faculty identify topics that need more emphasis in future courses. The key distinction is that summative assessments sum up learning after instruction, providing a comprehensive appraisal of student achievement at a particular point in time.

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## **Rubric**

A rubric is a structured scoring guide used to evaluate the quality of students’ work by clearly outlining the criteria and performance levels for an assignment or exam question​ (*inspera.com*). At its core, a rubric consists of a set of criteria (the aspects of performance being assessed, such as argument clarity, evidence, grammar for an essay question) and defined levels of achievement for each criterion (e.g., Excellent, Good, Fair, Poor) with descriptions of what performance looks like at each level​ (*inspera.com*).

Rubrics bring transparency and consistency to grading: they communicate expectations to students and ensure instructors assess work by the same standards for every student. In use, an analytic rubric breaks down the score by multiple criteria (giving separate scores for content, organization, mechanics, etc.), whereas a holistic rubric provides a single overall score based on an overall impression of the work. For example, an instructor might use an analytic rubric to grade a short-answer question, awarding points for accuracy, explanation, and clarity separately. By using rubrics, educators can provide detailed feedback, make grading more efficient and objective, and help students understand how to improve their performance on future assessments.

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## **Bloom's Taxonomy**

Bloom's Taxonomy is a hierarchical framework for classifying educational learning objectives (and by extension, assessment questions) into levels of complexity and cognitive skill. Originally developed by Benjamin Bloom and colleagues, it provides a common language for educators to discuss and design assessments and learning outcomes​ (*fctl.ucf.edu*).

The taxonomy’s cognitive domain is typically depicted in six levels, from lower-order thinking skills to higher-order thinking skills: Knowledge/Remember, Comprehension/Understand, Application, Analysis, Synthesis/Evaluate, and Creation (note: the taxonomy was revised in 2001, slightly changing some categories). In the context of quizzes and exams, Bloom’s Taxonomy helps instructors ensure a balance of question types—from basic recall of facts (lower level) to application of concepts or evaluation of scenarios (higher level)​ (*fctl.ucf.edu*).

For example, a lower-level question might ask a student to list or define a term, while a higher-level question might present a problem scenario and ask the student to analyze data or propose a solution. By designing assessments that target various levels of Bloom’s Taxonomy, educators encourage deeper learning and critical thinking, not just memorization. This alignment of questions with cognitive levels also helps in constructive alignment (aligning learning outcomes, learning activities, and assessments) to ensure that if we expect higher-order thinking from students, our assessments indeed require those skills.

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## **Authentic Assessment**

Authentic assessment is an approach to evaluation where students must apply their knowledge and skills to real-world tasks or problems, demonstrating meaningful understanding in contexts that professionals or citizens might encounter​ (*cetl.uconn.edu*). Unlike traditional assessments (e.g., multiple-choice tests) that often ask students to select an answer, authentic assessments typically involve generating responses or products—for example, conducting a scientific experiment, writing a policy memo, creating a business proposal, or developing a portfolio. The key idea is that students "perform a task" that mirrors real-life challenges rather than just recall information​ (*cetl.uconn.edu*).

This form of assessment is often considered performance-based or an alternative assessment method​ (*cetl.uconn.edu*). In higher education, authentic assessments might include clinical simulations in nursing, case study analyses in business, or design projects in engineering. They are usually evaluated with rubrics, since complex performances are judged on multiple criteria​ (*cetl.uconn.edu*).

The benefit of authentic assessment is that it tests students’ ability to integrate and apply what they've learned in a practical way, thereby preparing them for real situations beyond the classroom. It also tends to increase student engagement, as learners see the relevance of their work to their field or life goals.

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## **Learning Management System (LMS)**

A Learning Management System (LMS) is a software platform for administering, delivering, and tracking educational courses and assessments in an online or blended environment​ (*techtarget.com*). In higher education, the LMS is the digital hub of a course—it’s where instructors upload content (lecture slides, readings, videos), students submit assignments, and crucially, quizzes and exams are administered.

An LMS typically provides tools for creating online quizzes (with various question types), facilitating discussions, posting grades, and monitoring student progress​ (*techtarget.com*). It also often integrates interactive features like discussion forums or video conferencing to support learning​ (*techtarget.com*).

Examples of widely used LMS platforms in higher ed include Blackboard, Canvas, and Moodle, among others​ (*techtarget.com*). For instance, a professor might use Canvas to set up weekly reading quizzes that students take online, with the LMS automatically grading multiple-choice questions and recording those scores in the digital gradebook.

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## **Adaptive Learning**

Adaptive learning refers to educational systems or platforms that dynamically adjust the content, level, or sequence of instruction based on an individual student's performance in real time (*everylearnereverywhere.org*). Using algorithms and often artificial intelligence, adaptive learning software personalizes the learning path for each student by assessing their mastery of concepts and then modifying upcoming lessons or questions to address their specific needs (*everylearnereverywhere.org*).

In the context of quizzes and assessments, an adaptive learning system might give a student easier or more foundational questions if they are struggling, or move to advanced challenges if they are excelling, thus adapting to their skill level. One common application is adaptive testing, where the difficulty of test questions increases or decreases depending on the test-taker’s previous answers, aiming to pinpoint the learner’s level efficiently (as seen in exams like the GMAT). For example, in an online math course, if a student consistently misses algebra questions involving quadratic equations, the adaptive platform will provide additional practice and instructive feedback on that specific topic before moving on.

Adaptive learning technologies can make the educational experience more personalized and equitable—students get reinforcement on topics they haven’t mastered and can skip redundant practice for concepts they grasp, which keeps them appropriately challenged. However, designing effective adaptive systems requires a robust question bank and data analytics to ensure the recommendations truly benefit learning.

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## **Academic Integrity**

Academic integrity is the commitment to honesty, trust, fairness, respect, and responsibility in an academic setting (*monash.edu*). For students and faculty, it means upholding ethical standards in all scholarly work—for example, not cheating on exams, not plagiarizing assignments, and accurately citing sources.

In the context of quizzes and exams in higher education, academic integrity translates to students doing their own work under the stated rules and instructors creating conditions that discourage misconduct. Universities often promote academic integrity through honor codes (student-enforced codes of conduct) and by using technical tools and proctoring strategies to prevent cheating.

With the rise of online assessments, maintaining integrity has become a special challenge: “online assessments create new opportunities for cheating,” so instructors must employ multiple strategies to deter and detect it (*sigmaos.com*). These strategies include online proctoring (using live or AI-driven invigilators during remote exams), lockdown browsers that restrict computer activity during a test, and plagiarism detection software (like Turnitin) to check if written responses are original.

Academic integrity is crucial not only for fairness but for learning itself—if a student cheats, they bypass the feedback loop that assessment provides. Therefore, both cultural expectations (instilling why integrity matters) and practical measures (to catch or prevent cheating) are used in tandem to uphold academic integrity in higher ed assessments.

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## **Learning Analytics**

Learning analytics involves the measurement, collection, analysis, and reporting of data about learners and their contexts, with the aim of understanding and optimizing learning and the environments in which it occurs (*solaresearch.org*). In simpler terms, it means using data from students’ interactions (with quizzes, assignments, discussion forums, etc.) to gain insights that can improve teaching and learning.

In the realm of quizzes and exams, learning analytics might look at metrics such as quiz scores, time spent on questions, number of attempts, or which distractors were frequently chosen on multiple-choice items. For example, an instructor might analyze quiz results to identify which questions most students missed, indicating topics that may need to be revisited in class.

At an institutional level, learning analytics dashboards in an LMS can flag students who consistently perform poorly on early assessments, allowing advisors to intervene with support before the student falls too far behind. One well-known use of learning analytics is predicting student outcomes: by analyzing patterns (like a combination of low quiz participation and low scores), the system might predict which students are “at-risk” academically (*solaresearch.org*). These insights enable targeted interventions, such as recommending tutoring or additional practice for those students.

While learning analytics offers powerful opportunities to personalize education and improve curriculum design, it also raises considerations about data privacy and the importance of interpreting data cautiously (correlation is not always causation). Nonetheless, when used thoughtfully, learning analytics can close the feedback loop by informing educators how to adjust instruction and guiding students on where to focus their efforts.

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## **ChatGPT**

ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI that uses natural language processing to engage in human-like dialogue and generate written content (*techtarget.com*). Technically, it is a large language model capable of answering questions, writing essays or code, and carrying on conversations based on prompts given by the user (*techtarget.com*).

In the context of higher education, and assessments specifically, ChatGPT (and similar generative AI tools) has a dual significance: it can be a tool for learning and productivity, but also a challenge for academic integrity. On one hand, instructors and instructional designers are exploring positive use cases—for example, using ChatGPT to generate practice quiz questions, to get explanations of solutions (like an on-demand tutor), or to help faculty brainstorm assessment ideas (*edutopia.org*). On the other hand, ChatGPT has raised concerns because students might use it to produce answers or entire essays, potentially violating academic honesty policies.

Since ChatGPT can produce fluid, well-structured responses, educators are rethinking take-home exams and written assessments: many have begun designing more authentic or oral assessments, or using AI detectors (with mixed success) to mitigate cheating. The advent of ChatGPT in late 2022 prompted universities to issue guidelines on its use; some encourage teaching students about it and even integrating it into assignments (with proper attribution and critical evaluation), while others have restricted its use during exams.

In summary, ChatGPT represents the growing presence of AI in education—it’s a powerful assistant for generating and explaining content, but its misuse in quizzes and exams needs to be managed through updated policies, student guidance, and innovative assessment design.

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### **Open-Book Exam**

An open-book exam is an assessment format in which students are allowed to refer to course materials—such as textbooks, notes, or other approved resources—while completing the exam (cmu.edu). The purpose of an open-book exam is typically to evaluate higher-order thinking skills, such as application, analysis, or synthesis, rather than rote memorization.

In higher education, open-book exams are often used to simulate real-world problem solving, where access to information is expected. For instance, a law professor might provide a hypothetical legal case and ask students to analyze it using statutes and case law from their materials. These exams may still be time-limited and require preparation, as success depends on understanding where and how to find relevant information quickly, not simply on possessing it.

Open-book exams are especially effective when designed to test conceptual understanding and critical thinking. However, if the questions are too fact-based, students may rely heavily on copying or searching rather than engaging with the material. Well-crafted open-book assessments encourage students to organize their resources, recognize key concepts, and apply knowledge meaningfully.

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### **Closed-Book Exam**

A closed-book exam is a traditional assessment format in which students must complete the test without access to notes, textbooks, or other aids. These exams are designed to assess students’ internalization of course material—particularly their ability to recall facts, concepts, and processes from memory (cmu.edu).

In practice, closed-book exams are common in large lecture-based courses and introductory-level classes, where foundational knowledge is emphasized. For example, an instructor in a biology course might administer a multiple-choice closed-book midterm to test students' understanding of key cellular processes or vocabulary.

The benefit of closed-book exams lies in their efficiency and standardization. They allow for large-scale, consistent measurement of learning outcomes. However, critics argue that they often emphasize surface learning—memorization over meaningful understanding—and may not reflect how knowledge is applied outside the classroom. As a result, educators are increasingly blending closed-book formats with other assessment types to better assess deep learning.

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### **Take-Home Exam**

A take-home exam is an assessment students complete outside of class, typically over a longer period—ranging from a few hours to several days. These exams are designed to assess critical thinking, synthesis of ideas, and the ability to construct well-supported arguments or perform complex problem-solving tasks (carleton.edu).

Take-home exams can vary widely in format: they might consist of essay questions, problem sets, or case analyses. For instance, a political science course might assign a take-home exam where students compare political theories using evidence from readings and lectures. While students have access to all their course materials, the time constraints and expectations for original, thoughtful responses elevate the challenge.

Because take-home exams are unsupervised, academic integrity is a central concern. Instructors often mitigate this by designing questions that require personalized or open-ended responses, integrating plagiarism detection software, and clearly communicating expectations. When implemented effectively, take-home exams allow students to demonstrate mastery at a higher cognitive level than in-class tests.

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### **Oral Exam**

An oral exam is an assessment format where students verbally respond to questions posed by an instructor or panel, either in person or via video conference. This format is often used to evaluate a student’s depth of understanding, ability to explain reasoning, and capacity to engage in scholarly dialogue (uwo.ca).

Oral exams are common in graduate-level education, language proficiency testing, and professional programs such as medicine or law. For example, a Ph.D. candidate might undergo a qualifying exam in which they defend their knowledge of major theories and methodologies in their field. Similarly, a nursing student might be asked to explain a clinical decision-making process during a simulated scenario.

One major advantage of oral exams is their adaptability: instructors can probe more deeply based on student responses, clarify ambiguity, and assess reasoning in real time. They also reduce opportunities for cheating. However, oral exams may introduce anxiety or bias, and their effectiveness depends on clear rubrics and trained examiners. When well-structured, oral exams can powerfully measure complex learning and communication skills.

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### **Objective Test**

An objective test is an assessment composed of questions that have clear, correct answers and can be scored consistently and reliably, usually without subjective interpretation (cmu.edu). Common formats include multiple-choice, true/false, matching, and fill-in-the-blank questions.

Objective tests are widely used in higher education for evaluating large cohorts of students efficiently. For example, an economics instructor might use a multiple-choice final exam to assess understanding of key models, graphs, and definitions. These tests are typically administered via scantron or online systems and are especially popular in standardized testing.

Because objective tests can cover a broad range of material in a short time, they are useful for assessing breadth of knowledge. However, they often emphasize recall over higher-order thinking unless designed with care. Well-constructed objective questions can go beyond simple memorization—for instance, by asking students to apply formulas, interpret data, or analyze scenarios.

The primary strengths of objective tests are efficiency, consistency in scoring, and scalability. Nonetheless, they are usually complemented by subjective assessments (e.g., essays, projects) that evaluate skills like synthesis, creativity, and argumentation.

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## **Formative Assessment**

Formative assessment refers to low-stakes evaluations conducted during the learning process to monitor student understanding and provide ongoing feedback for improvement. Unlike assessments designed solely to assign grades, formative assessments identify strengths and weaknesses in real time, allowing instructors to adjust their teaching methods and enabling students to modify their learning strategies.

Common examples of formative assessments include ungraded quizzes, one-minute papers, class discussions, drafts, concept maps, or in-class polling. For instance, an instructor might use a quick, ungraded quiz mid-lecture to gauge whether students grasp a concept and then use this information to clarify misconceptions immediately.

Because formative assessments typically have little or no point value, they foster a supportive learning environment where making mistakes is considered an integral part of the learning process.

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## **Summative Assessment**

Summative assessment refers to high-stakes evaluations of student learning conducted at the end of an instructional period, such as the conclusion of a unit, course, or semester. The primary purpose of summative assessments is to measure whether students have achieved specific learning outcomes or standards by comparing their performance against predetermined benchmarks.

Typical examples in higher education include midterm exams, final exams, research papers, final projects, or capstone presentations. These assessments generally carry significant weight in determining a student's final grade and offer a comprehensive evaluation of student learning and achievement at a specific point in time.

While summative assessments are primarily evaluative, educators often use their results to inform future instruction. Analyzing final exam performance, for example, can help instructors identify content areas needing greater emphasis or revision in future offerings of the course.

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## **Diagnostic Assessment**

Diagnostic assessment involves evaluations designed specifically to identify students' existing knowledge, skills, strengths, and weaknesses before instruction begins or at key points throughout a learning experience. The primary purpose is to diagnose specific areas of need so that instruction can be tailored accordingly.

Diagnostic assessments are typically conducted at the beginning of a course, unit, or lesson. Examples include pre-tests, placement exams, skills inventories, or self-assessment questionnaires. For example, a mathematics instructor might administer a pre-test covering algebra fundamentals to determine students' preparedness and adapt instruction based on identified gaps or strengths.

Unlike formative assessments, which are ongoing, diagnostic assessments usually occur at strategic points to establish a baseline or pinpoint specific instructional needs. This targeted insight helps instructors offer individualized support and enhances students' readiness for new material.

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## **Authentic Assessment**

Authentic assessment involves evaluation methods that require students to apply their knowledge and skills to real-world tasks, problems, or scenarios. Rather than merely testing recall through traditional methods like multiple-choice exams, authentic assessments require students to demonstrate their competencies in practical, relevant contexts similar to what they might encounter in professional or civic life.

Common forms of authentic assessment in higher education include clinical simulations in nursing, business case studies, design projects in engineering, research presentations, or writing policy memos. For instance, students in a business course might develop and present a business proposal rather than completing a traditional written exam.

Authentic assessments are frequently evaluated using rubrics because of their complexity and the need to assess multiple performance criteria. This assessment approach promotes deeper engagement, critical thinking, and greater alignment between academic learning and real-world applications, motivating students by highlighting the relevance of their coursework.

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## **Performance-Based Assessment**

Performance-based assessment involves methods where students actively demonstrate their knowledge, understanding, and abilities through performing specific tasks rather than selecting responses from pre-constructed options like multiple-choice questions. This assessment approach emphasizes applying knowledge in practical and authentic contexts, often involving complex, open-ended tasks.

Examples of performance-based assessments include oral presentations, scientific experiments, musical or theatrical performances, artistic creations, or demonstrations of technical skills. For instance, a biology student might design and carry out a laboratory experiment and then present their findings to demonstrate understanding of the scientific method and relevant concepts.

Performance-based assessments typically require clearly defined criteria or rubrics to objectively evaluate student performance. The benefits include deeper student engagement, demonstration of real-world competence, improved problem-solving abilities, and assessment of higher-order cognitive skills like analysis, synthesis, and creation.

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### **Subjective Assessment**

Subjective assessment refers to evaluation methods that rely on human judgment to interpret and score student work, often based on qualitative criteria rather than fixed answers (carleton.edu). These assessments are typically open-ended, requiring students to construct responses—such as essays, presentations, or portfolios—that demonstrate their understanding, reasoning, and creativity.

In higher education, subjective assessments are commonly used in disciplines like the humanities, social sciences, and the arts. For example, a literature professor might ask students to analyze a novel’s themes in a written essay, evaluating the depth of insight and the clarity of argument. The scoring process typically involves rubrics or guidelines to ensure fairness and consistency, but the evaluator’s interpretation remains central.

While subjective assessments allow for richer demonstrations of learning, they also introduce potential variability in grading. Clear rubrics, multiple evaluators, and anonymized grading can help mitigate bias. The advantage of subjective assessments is their ability to capture nuanced thinking, complex synthesis, and originality—elements that objective tests often miss.

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### **High-Stakes Assessment**

A high-stakes assessment is a test or evaluation that carries significant consequences for a student, such as determining final grades, course advancement, or graduation eligibility (carleton.edu). Because of their impact, these assessments often generate pressure and require extensive preparation.

Examples in higher education include final exams, standardized certification tests, licensure exams, and comprehensive theses or dissertations. For instance, a final cumulative exam in a chemistry course that accounts for 50% of the course grade would be considered high-stakes. These assessments are typically summative and are used to make important decisions about student performance.

High-stakes testing can motivate students to study and perform, but it can also provoke anxiety, especially if students feel their future depends on a single event. Critics argue that such assessments may not always reflect true learning and can disadvantage certain groups. As a result, many educators advocate balancing high-stakes tests with ongoing, low-stakes assessments that provide a fuller picture of student learning.

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### **Low-Stakes Assessment**

Low-stakes assessment refers to evaluations that have minimal impact on a student’s final grade or academic standing but are intended to provide feedback, practice, and opportunities for growth (carleton.edu). These assessments are designed to encourage learning through experimentation and reflection without the pressure of high penalties.

In the classroom, low-stakes assessments might include short in-class quizzes, drafts of essays, one-minute reflections, or ungraded peer reviews. For example, a professor might assign weekly quizzes worth a small percentage of the final grade to help students keep up with readings and reinforce key concepts.

Low-stakes assessments promote a growth mindset by allowing students to make mistakes and learn from them. They are especially effective when paired with feedback, helping students identify gaps in knowledge before they face higher-stakes evaluations. Research also suggests that frequent low-stakes testing can improve long-term retention through the “testing effect”—the phenomenon where retrieving information helps solidify learning.

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### **Placement Test**

A placement test is an assessment administered before a student begins a course or academic program to determine their appropriate level of instruction (nacada.ksu.edu). The goal is not to grade past learning but to place students in the right course or track where they are most likely to succeed.

In higher education, placement tests are common in subjects like math, writing, or foreign languages. For example, a student entering college might take a math placement exam to determine whether they should begin with college algebra or a more advanced course. These tests may be locally developed or standardized and are often administered online during orientation or pre-enrollment periods.

Effective placement testing ensures students are neither under-challenged nor overwhelmed, helping institutions tailor instruction to students’ readiness. Some schools are now exploring alternatives to traditional placement tests, such as using high school GPA or multiple measures to guide course placement, especially in the wake of equity concerns around standardized exams.

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### **Standardized Test**

A standardized test is a formal assessment administered and scored in a consistent, uniform manner, often designed to compare performance across individuals, institutions, or regions (edglossary.org). These tests feature fixed question formats, clear scoring rubrics, and administration protocols to ensure comparability.

In higher education, standardized tests can serve a variety of purposes: admissions (e.g., SAT, GRE), placement (e.g., ACCUPLACER), certification (e.g., NCLEX for nurses), or program evaluation. For instance, an education department might require students to pass the Praxis exam before advancing to student teaching.

Supporters argue that standardized tests offer objectivity and broad benchmarking, especially for assessing large populations. However, critics raise concerns about cultural bias, teaching to the test, and overemphasis on narrow skills. As a result, many universities have adopted test-optional policies or sought more holistic approaches to student evaluation.

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### **Benchmark Assessment**

Benchmark assessment refers to periodic testing conducted at set intervals during a course or academic year to evaluate student progress toward specific learning goals or standards (nwea.org). These assessments are often standardized across a course or institution and are designed to inform instruction, identify learning gaps, and predict performance on future high-stakes assessments.

In higher education, benchmark assessments might take the form of a common writing assignment scored across all sections of a composition course or a departmental diagnostic in an introductory STEM course. For instance, a biology department might administer the same concept inventory test in week 5 of every Bio 101 class to track how students are progressing relative to course outcomes.

Unlike formative assessments, which happen continuously and are often informal, benchmark assessments are structured and occur at key points in time—usually at the beginning, middle, or near the end of a course. They provide data for instructors, departments, and administrators to make curricular adjustments and to support student success on a broader scale.

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### **Continuous Assessment**

Continuous assessment is an approach to evaluation that integrates frequent, varied assessments throughout a course to provide an ongoing picture of student learning (teaching.unsw.edu.au). Instead of relying on one or two major exams, continuous assessment distributes assessment tasks—such as quizzes, reflections, lab reports, and participation—across the term.

In higher education, this model supports a “little and often” philosophy: each task contributes a small percentage to the final grade, encouraging consistent engagement. For example, a psychology course might use weekly online quizzes (10%), discussion posts (10%), a midterm (30%), and a final project (50%)—together forming a continuous assessment framework.

The advantages of continuous assessment include reduced test anxiety, better retention through spaced learning, and more opportunities for feedback. It supports students who may not perform well in high-stakes testing and allows instructors to monitor progress in real time. However, it requires careful workload planning and clear communication of grading policies.

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### **Final Exam**

A final exam is a comprehensive, high-stakes assessment administered at the end of a course to evaluate cumulative student learning across the full term (cmu.edu). It often covers all major topics, theories, or skills taught and is typically weighted heavily in the overall course grade.

Final exams may be written (e.g., essay, multiple choice, short answer), oral, practical (e.g., lab skills demonstration), or digital. In a history course, for instance, a final exam might require students to write an analytical essay comparing two historical periods, drawing on multiple sources covered throughout the semester.

The purpose of a final exam is both summative and diagnostic—it confirms whether students have met the intended learning outcomes and, in some cases, signals mastery for progression or graduation. While some instructors prefer projects or portfolios in place of finals, traditional exams remain common in many disciplines. Effective final exams are aligned with course objectives and challenge students at multiple cognitive levels.

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### **Midterm Exam**

A midterm exam is an assessment conducted approximately halfway through a course to evaluate students’ understanding of the material covered in the first part of the term (cmu.edu). It serves as a checkpoint for both students and instructors, providing feedback on academic progress and identifying concepts that may need reinforcement.

In practice, midterms can vary in format: a philosophy course might assign a midterm essay that asks students to compare key thinkers, while a chemistry course might give a timed, multiple-choice and calculation-based test. The midterm often accounts for a substantial portion of the final grade—typically 20–30%.

Midterms help students adjust their study habits and identify gaps in their knowledge before the final exam. For instructors, the results can inform pacing, re-teaching, or modifying upcoming lessons. When well-designed, midterms reinforce course continuity and support metacognitive awareness in learners.

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### **Bloom's Taxonomy**

Bloom’s Taxonomy is a hierarchical framework for categorizing learning objectives and cognitive skills, often used to design instruction and assessment in education (fctl.ucf.edu). Developed by Benjamin Bloom and colleagues in 1956—and revised in 2001—it classifies cognitive learning into six levels, from basic recall to complex creation.

The revised taxonomy is often presented as:

1. **Remember** – recalling facts or concepts

2. **Understand** – explaining ideas

3. **Apply** – using information in new situations

4. **Analyze** – examining components and relationships

5. **Evaluate** – making judgments based on criteria

6. **Create** – producing original work

In assessment design, Bloom’s Taxonomy guides instructors to vary the cognitive demands of exam questions. For example, a “Remember” question might ask students to define a term, while a “Create” task could require developing a new model or solution. Balanced assessments target multiple levels to encourage deep learning and align closely with intended learning outcomes.

By integrating Bloom’s Taxonomy into quiz or exam design, educators can move beyond surface learning and assess students’ critical thinking, problem-solving, and creativity.

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### **Learning Outcome**

A learning outcome is a clear, measurable statement that describes what a student is expected to know, do, or value as a result of a learning experience (carleton.edu). Learning outcomes guide the design of curricula, assessments, and instructional activities by articulating the intended results of teaching in terms of student performance.

In higher education, learning outcomes are typically written using action verbs that reflect different levels of cognitive complexity—often aligned with Bloom’s Taxonomy. For example, a learning outcome in a biology course might read: “Students will be able to analyze experimental data and draw valid conclusions about gene expression.” These outcomes are distinct from learning goals, which are broader and less measurable.

Well-crafted learning outcomes provide a roadmap for both instructors and students. They ensure that teaching activities and assessments are purposeful, and they support transparency by clearly communicating expectations. Outcomes also help departments assess course or program effectiveness by offering concrete criteria against which student achievement can be measured.

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### **Backward Design**

Backward design is an instructional planning approach that begins with identifying the desired learning outcomes and then works backward to develop assessments and learning activities that align with those outcomes (Wiggins & McTighe, 2005). This model contrasts with more traditional approaches that start with content coverage or textbook chapters.

In higher education, backward design encourages faculty to first ask: “What should students be able to do by the end of this course?” Once outcomes are defined, instructors design assessments that provide evidence of mastery, and only then do they plan lectures, readings, and activities. For example, if a course outcome is “evaluate competing policy proposals,” the assessment might be a policy brief or debate, rather than a standard quiz.

The benefit of backward design is coherence: learning outcomes, teaching strategies, and assessments are aligned and mutually reinforcing. This structure promotes deeper learning, as every course element serves a clearly defined purpose. Backward design is also foundational to outcome-based education and effective curriculum development.

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### **Constructive Alignment**

Constructive alignment is a curriculum design principle that ensures coherence among intended learning outcomes, learning activities, and assessment methods (Biggs, 1996). The idea is that all components of a course should work together to support students in achieving the desired outcomes.

The term “constructive” refers to students actively constructing knowledge through relevant tasks, while “alignment” refers to the coordination of outcomes, teaching strategies, and assessments. For instance, if a course outcome is for students to “critically evaluate historical sources,” then students might analyze primary texts in discussion sections and be assessed through a source-based essay—aligning the activity and the evaluation method with the stated goal.

Constructive alignment helps avoid mismatches between what instructors teach, what they test, and what students are supposed to learn. It is widely used in curriculum design and accreditation processes because it promotes transparency, accountability, and student-centered learning. When done well, it increases the likelihood that students will reach higher levels of achievement.

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### **Rubric**

A rubric is a structured scoring guide that outlines the criteria for evaluating student work and defines levels of performance for each criterion (inspera.com). Rubrics help ensure that grading is transparent, consistent, and aligned with learning goals.

In higher education, rubrics are used for essays, presentations, projects, and other complex tasks where multiple aspects of quality are assessed. For example, a rubric for a research paper might include criteria such as thesis clarity, use of evidence, organization, and mechanics, with performance levels ranging from “Excellent” to “Needs Improvement.” Each level includes descriptive indicators, so students and instructors understand what is expected.

Rubrics can be **analytic** (scoring each criterion separately) or **holistic** (assigning a single score based on overall quality). They serve multiple purposes: guiding students as they complete assignments, streamlining grading for instructors, and providing targeted feedback. Rubrics are especially useful in large courses or multi-section programs where consistent evaluation is essential.

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### **Analytic Rubric**

An analytic rubric is a type of scoring tool that breaks down an assignment into multiple criteria and evaluates each criterion separately, typically using a grid format (teachingcommons.stanford.edu). Each row represents a different aspect of performance (e.g., organization, evidence, grammar), and each column corresponds to a level of achievement (e.g., Exemplary, Proficient, Developing).

For example, in a philosophy paper, one criterion might be “Argument Structure,” with descriptors for each level like “Clear and logically ordered” (Exemplary) or “Confusing and disjointed” (Developing). Instructors score each row independently and then sum the values for a final grade.

Analytic rubrics provide detailed, criterion-specific feedback to students and are ideal when instructors want to communicate strengths and weaknesses across multiple dimensions of performance. Compared to holistic rubrics, analytic rubrics take more time to develop and score but offer greater precision and fairness. They are particularly useful for high-stakes assignments, program assessment, and skill development over time.

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Let me know when you're ready for the next five\!

Absolutely—here’s the next polished set of glossary entries in your signature style:

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### **Learning Outcome**

A learning outcome is a clear, measurable statement that describes what a student is expected to know, do, or value as a result of a learning experience (carleton.edu). Learning outcomes guide the design of curricula, assessments, and instructional activities by articulating the intended results of teaching in terms of student performance.

In higher education, learning outcomes are typically written using action verbs that reflect different levels of cognitive complexity—often aligned with Bloom’s Taxonomy. For example, a learning outcome in a biology course might read: “Students will be able to analyze experimental data and draw valid conclusions about gene expression.” These outcomes are distinct from learning goals, which are broader and less measurable.

Well-crafted learning outcomes provide a roadmap for both instructors and students. They ensure that teaching activities and assessments are purposeful, and they support transparency by clearly communicating expectations. Outcomes also help departments assess course or program effectiveness by offering concrete criteria against which student achievement can be measured.

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### **Backward Design**

Backward design is an instructional planning approach that begins with identifying the desired learning outcomes and then works backward to develop assessments and learning activities that align with those outcomes (Wiggins & McTighe, 2005). This model contrasts with more traditional approaches that start with content coverage or textbook chapters.

In higher education, backward design encourages faculty to first ask: “What should students be able to do by the end of this course?” Once outcomes are defined, instructors design assessments that provide evidence of mastery, and only then do they plan lectures, readings, and activities. For example, if a course outcome is “evaluate competing policy proposals,” the assessment might be a policy brief or debate, rather than a standard quiz.

The benefit of backward design is coherence: learning outcomes, teaching strategies, and assessments are aligned and mutually reinforcing. This structure promotes deeper learning, as every course element serves a clearly defined purpose. Backward design is also foundational to outcome-based education and effective curriculum development.

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### **Constructive Alignment**

Constructive alignment is a curriculum design principle that ensures coherence among intended learning outcomes, learning activities, and assessment methods (Biggs, 1996). The idea is that all components of a course should work together to support students in achieving the desired outcomes.

The term “constructive” refers to students actively constructing knowledge through relevant tasks, while “alignment” refers to the coordination of outcomes, teaching strategies, and assessments. For instance, if a course outcome is for students to “critically evaluate historical sources,” then students might analyze primary texts in discussion sections and be assessed through a source-based essay—aligning the activity and the evaluation method with the stated goal.

Constructive alignment helps avoid mismatches between what instructors teach, what they test, and what students are supposed to learn. It is widely used in curriculum design and accreditation processes because it promotes transparency, accountability, and student-centered learning. When done well, it increases the likelihood that students will reach higher levels of achievement.

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### **Rubric**

A rubric is a structured scoring guide that outlines the criteria for evaluating student work and defines levels of performance for each criterion (inspera.com). Rubrics help ensure that grading is transparent, consistent, and aligned with learning goals.

In higher education, rubrics are used for essays, presentations, projects, and other complex tasks where multiple aspects of quality are assessed. For example, a rubric for a research paper might include criteria such as thesis clarity, use of evidence, organization, and mechanics, with performance levels ranging from “Excellent” to “Needs Improvement.” Each level includes descriptive indicators, so students and instructors understand what is expected.

Rubrics can be **analytic** (scoring each criterion separately) or **holistic** (assigning a single score based on overall quality). They serve multiple purposes: guiding students as they complete assignments, streamlining grading for instructors, and providing targeted feedback. Rubrics are especially useful in large courses or multi-section programs where consistent evaluation is essential.

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### **Analytic Rubric**

An analytic rubric is a type of scoring tool that breaks down an assignment into multiple criteria and evaluates each criterion separately, typically using a grid format (teachingcommons.stanford.edu). Each row represents a different aspect of performance (e.g., organization, evidence, grammar), and each column corresponds to a level of achievement (e.g., Exemplary, Proficient, Developing).

For example, in a philosophy paper, one criterion might be “Argument Structure,” with descriptors for each level like “Clear and logically ordered” (Exemplary) or “Confusing and disjointed” (Developing). Instructors score each row independently and then sum the values for a final grade.

Analytic rubrics provide detailed, criterion-specific feedback to students and are ideal when instructors want to communicate strengths and weaknesses across multiple dimensions of performance. Compared to holistic rubrics, analytic rubrics take more time to develop and score but offer greater precision and fairness. They are particularly useful for high-stakes assignments, program assessment, and skill development over time.

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Let me know when you're ready for the next five\!

Great list\! Here are my favorite five from that set, chosen for their importance in assessment design and their relevance to quiz-based pedagogy in higher education. I’ve given them the full treatment in your preferred format:

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### **Item Difficulty**

Item difficulty refers to how challenging a particular test question is for a group of students, typically expressed as the proportion of test-takers who answer the item correctly (ets.org). It is one of the most basic yet essential metrics in test analysis, helping instructors gauge whether a question is too easy, too hard, or appropriately calibrated.

In practice, an item answered correctly by 90% of students has high item difficulty (i.e., it’s easy), while one answered correctly by only 30% is considered more difficult. This statistic is especially useful when analyzing the effectiveness of quizzes or exams—questions that everyone gets right might not discriminate between levels of understanding, whereas questions that no one gets right may be poorly worded or cover material not taught.

Item difficulty should ideally vary across a test, with a mix of easier and harder questions to support a range of student abilities. When building or refining a question bank, instructors often use item difficulty data to improve balance and challenge in future assessments.

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### **Item Discrimination**

Item discrimination is a measure of how well a test question distinguishes between high-performing and low-performing students on an overall assessment (nwea.org). A well-discriminating item is one that students who score high on the entire test tend to get right, while lower-performing students tend to miss.

This metric is crucial for determining the quality of an assessment. For example, if both high- and low-performing students are equally likely to get a question right or wrong, it may indicate that the question is too ambiguous, too easy, or unrelated to the intended outcomes. A high discrimination index suggests that the item effectively contributes to assessing meaningful differences in student understanding.

Assessment designers use item discrimination statistics to revise or eliminate ineffective questions, particularly in high-stakes or standardized tests. In higher education, reviewing item discrimination can improve quiz validity and ensure that assessments truly reflect varying levels of mastery.

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### **Fairness (Assessment Fairness)**

Assessment fairness refers to the principle that all students should have an equal opportunity to demonstrate their knowledge and skills under conditions that are equitable and unbiased (aera.net). A fair assessment does not advantage or disadvantage students based on irrelevant characteristics such as background, identity, or access to resources.

In practice, fairness touches everything from question clarity and cultural relevance to accommodations for students with disabilities and the design of grading policies. For example, using a variety of assessment types—essays, quizzes, projects, presentations—can provide multiple ways for students to succeed and reduce bias toward any single mode of expression.

Ensuring fairness also means being transparent about expectations (e.g., through rubrics), allowing for feedback and revision, and reviewing assessments for potential cultural or linguistic bias. Instructors who prioritize fairness foster inclusive learning environments, support academic integrity, and build trust with students.

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### **Constructed-Response Item**

A constructed-response item is an assessment question that requires students to generate their own answers, rather than choosing from a list of options. These items include short-answer, essay, and fill-in-the-blank questions, and are valued for their ability to assess deeper understanding, reasoning, and communication skills (carleton.edu).

Unlike selected-response items (e.g., multiple choice), constructed-response formats allow students to demonstrate how they think through a problem or explain a concept in their own words. For example, an exam might ask, “Explain the difference between formative and summative assessment and provide an example of each.”

While constructed-response items can provide rich insight into student thinking, they also present challenges: they take longer to grade, are more subjective (unless using a rubric), and require clear scoring criteria. Still, they are essential for disciplines that prioritize argumentation, synthesis, and original thought.

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### **Rubric**

A rubric is a structured scoring guide used to evaluate the quality of students’ work by clearly outlining the criteria and performance levels for an assignment or exam question (inspera.com). At its core, a rubric consists of a set of criteria (the aspects of performance being assessed, such as argument clarity, evidence, or grammar for an essay question) and defined levels of achievement for each criterion (e.g., Excellent, Good, Fair, Poor) with descriptions of what performance looks like at each level (inspera.com).

Rubrics bring transparency and consistency to grading: they communicate expectations to students and ensure instructors assess work by the same standards for every student. In use, an **analytic rubric** breaks down the score by multiple criteria (giving separate scores for content, organization, mechanics, etc.), whereas a **holistic rubric** provides a single overall score based on an overall impression of the work. For example, an instructor might use an analytic rubric to grade a short-answer question, awarding points for accuracy, explanation, and clarity separately.

By using rubrics, educators can provide detailed feedback, make grading more efficient and objective, and help students understand how to improve their performance on future assessments.

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Let me know if you’d like the next five from the list (e.g., *Mastery Learning*, *Competency-Based Assessment*, etc.) or want this batch exported as Markdown or PDF\!
