Mastery Learning
The core of mastery learning (ML) is the idea that aptitude is not fixed but rather student aptitude equals time needed to learn a task (Gentile, 1997). Paralleling the simple definition of aptitude is the assumption that all students can learn. If time, aptitude, and an assumption of learning are the central constructs of ML then what does ML look like and what is needed to employ it in a classroom? Anderson (as cited in Gentile, 1997) indicated the following components as essential to utilize ML in the classroom. The six components are: (1) specified student or learning outcomes; (2) valid assessment techniques matching the outcomes; (3) set criteria for mastery; (4) sequenced curriculum units; (5) student feedback on their assessments; and (6) additional time and feedback for students who need to meet criteria for mastery. 
The following ML example of teaching qualitative research will illustrate how ML can be used to assess a unit of a course. Start with learning outcomes such as students will have the ability to define qualitative research, compare and contrast types of qualitative methods, and to collect and analyze qualitative data. Next create assessment procedures such as multiple choice tests, home work assignments, papers, and or projects. In conjunction with your assessment procedures determine criteria for mastery. This could be a minimum number of correct answers on an exam, creating specific points to address when critiquing qualitative methods, and or determining how much data to collect and procedures for analysis. Subsequently, structure your teaching unit into sections and integrate multiple assessments. Finally provide detailed feedback to your students throughout the process and provide additional time and guidance to students who did not demonstrate mastery of the material.  In addition to this example, ML can be applied to an entire course. The above criteria can be structured to illustrate learning at an average, above average, and exceptional level.
Another example of demonstrating mastery is requiring students to demonstrate a certain skill or knowledge base more than once. This could take the form of cumulative exams, demonstrating a skill in homework, projects, and portfolios, or connecting a skill to more than one class. When students demonstrate skill in multiple ways it provides addition opportunity for individual comprehension monitoring and instructor feedback. Moreover, it can reduce the chance of guessing the correct answer on an exam and superficially discussing a concept in a paper. ML can be employed in small, large, face to face, online, lecture, and or discovery courses. ML does not work well in classes that do not have clear student outcomes and a lack of criteria for mastery.
In summary, ML is an approach that emphasizes ongoing assessment throughout the unit or course, multiple opportunities for students to demonstrate mastery of content, and the assumption that students can learn. ML can be used to assess a single unit or an entire course and can be utilized to have students demonstrate continued mastery. Finally, ML can be employed to increase student comprehension monitoring skills.