1: Our Learners and How They Learn




For the first time in history, students from three generations could conceivably be found taking classes in college. These generations are the baby boomers, Gen-Xers, and millennials. This chapter provides an overview of the three generations, sans characteristics, explores what the terms traditional and nontraditional learners mean is in order. Mental models can range from a single piece of data to a bundle of related knowledge (know that), skills (know how), and a complex set of connections that are difficult to extract, but have to do with applying the information, or knowing when. The concept of cognitive load is built on the known limited capacity of working memory. Flavel identified three types of metacognition: knowledge about the self, knowledge about cognitive tasks, and strategic knowledge. The original intent of learning-style inventories was for students to understand how they learn best.


For the first time in history, students from three generations could conceivably be found taking classes in college. These generations are the baby boomers, Gen-Xers, and millennials. Much has been written about the Gen-Xers and millennials with regard to personality traits, expectations of life, how they learn, and other dimensions on how they differ from previous college students. I have been tempted to reiterate the information. However, other, more relevant forces are at work in higher education, such as moving education from the classroom to the online environment, a change that many faculty are ill-prepared for from a theoretical and practical perspective; recent findings about how the brain works with relation to learning; the use of handheld technology as peripheral cognitive storage; and the not-so-recent understandings from the field of cognitive psychology that are being translated into practice in higher education. Thus, I will resist the urge to summarize what has been written about these groups as the information seems to be a moving target. Instead, let me place these generations in perspective chronologically.

Before providing a brief overview of the three generations, sans characteristics, understanding what the terms traditional and nontraditional learners mean is in order. Not specifically defined, but almost universally understood without using the term “traditional,” these students enrolled full time in college immediately after graduation from high school and were financially dependent upon others, typically their parents. Conversely, the concept of the nontraditional student has been the focus of research on persistence and risk of attrition, although a consensus for a definition of the “nontraditional” student has not been reached (Chung, Turnbull, & Chur-Hansen, 2014). Studies completed for the National Center for Education Statistics (NCES; Radford, Cominole, & Skomsvold, 2015) used the following characteristics to identify undergraduate nontraditional learners:

  • Delaying college enrollment until age 24

  • Part-time enrollment

  • Working full time while attending school

  • Financially independent (i.e., not reliant on support from their parents)

  • Responsible for at least one dependent

  • Being a single parent

  • Earning a general equivalency diploma instead of a high school diploma

Findings from five separate NCES studies from 1995 to 2012 on the nontraditional learner have been consistent over time and indicate that 74% of college students can be defined as nontraditional.

The distinction between traditional and nontraditional students may have greater implications for teaching and learning than generational differences. Most online nursing programs involve either RN to bachelor of science in nursing (BSN) or graduate students. These students will, most likely, fit the definition of the nontraditional learner because they work more than 35 hours each week and are financially independent. They may also be single parents. This characterization has implications for the time they can devote to studying, which encourages faculty to choose teaching strategies and assessments strategically, as well as to design the learning management system (LMS) for intuitive navigation.

Baby Boomers

The baby boomers were born between 1945 and 1964, which means the youngest members of this group turned 50 in 2014. Some of these students may very well return to college for a second career or graduate study. Most likely, they are traditional learners who were taught in the classroom, where lecture was the main educational strategy used.

Many nursing faculty belong to this generation. Recent national statistics indicate that the average age of doctorally prepared nursing faculty, regardless of rank, and the average age of full professors at 61.6 years old (American Association of Colleges of Nursing [AACN], 2014). In addition, 50% of RNs—and therefore potential students—are older than 50 years of age, according to a survey by the National Council of the State Boards of Nursing (2013).


Gen-Xers, the children of the baby boomers, were so named because some authors felt, as a group, they lacked a generation-defining event (Wilson, 2002). Sandwiched in-between the boomers and millennials, they are considered traditional learners even though they grew up with technology. Although authors differ on exact dates, the Gen-Xers were born from the mid-1960s to the early 1980s. The oldest members of this group turned 50 in 2015.


The millennials were born between the early 1980s and 2004, although some authors are less solid on the dates. Technology has been with this group their entire lives, with many using computers from a very early age. The oldest members of this group are in their early 30s, the youngest in middle school.

A plethora of writing has been published on the Net Generation (Gen-Xers and millennials), much of it in disagreement, leaving the educator with few solid strategies to advise teaching. Instead of attempting to customize our teaching to meet disparate characteristics of our learners, our focus should be on applying effective learning theories, models, and concepts, regardless of how long they have been around; using teaching strategies that support how the mind learns, regardless of how old or what generation that mind belongs to; and using technology effectively and efficiently to support all of this. Thus, the focus of this chapter is on the learners and how they learn, introducing concepts that have been around for a while, but perhaps may be less familiar to nurse educators. In addition, I discuss perhaps even less familiar concepts to promote learning from cognitive science research.

The Net Generation

Another means of identifying traditional and nontraditional learners is how they learn, which for the Gen-Xers and millennials is technology based, leading many to prefer the more descriptive term of Net Generation for these two generational groups. Although not true for all members of the Net Generation, they have come to rely on technology in all aspects of their lives, including learning, which led Rosen (2011) to define a subgroup of the Net Generation born in the 1990s that he calls the iGeneration. For this group, technology is not something special to be used under specific circumstances. Instead, all forms of technology—laptops, tablets, smartphones, e-readers, and so forth—are extensions of who they are; a very different perspective from how the telephone was thought of by the baby boomers. In Rosen’s (2011) view, members of the iGeneration “don’t question the existence of technology and media. They expect technology to be there, and they expect it to do whatever they want it to do. Their WWW doesn’t stand for World Wide Web; it stands for Whatever, Whenever, Wherever” (para. 7).

This perspective has the potential to forever alter how we teach and the definition of learning in general. Having the answer to almost any question a few clicks away by using a handheld device of some sort, learners have essentially added external brain capacity. This may alleviate the need to teach many facts and concepts, instead shifting the focus of education to critical thinking, critical appraisal, and the ability to distinguish reliable resources from those that are not. With this somewhat radical thought in mind, a review of what is currently known about how we learn is in order.


Learning occurs because of the interaction among attention, thinking, and memory. One cannot learn for the long term without attending to the lessons, engaging cognitive and metacognitive processes, and encoding and storing whatever was attended to in long-term memory (LTM). Our understanding of how learning occurs has shifted from what Miller (2014) refers to as the three-box theory of memory, specifically working memory, short-term memory (STM), and LTM, to that of the relationship of attention to memory.

The Three-Box Theory

Research on memory is in a bit of a flux, with the three-box theory falling out of favor and an explanation understandable to educators yet to emerge from cognitive research (Miller, 2014). The process originally introduced by Atkinson and Shiffrin (1968), which Miller (2014) refers to as the three-box theory, was composed of three subsystems, “sensory register, short-term store, and the long-term store” (p. 16), now known as working memory, STM, and LTM. Although gaps in our understanding exist, which explain how observations from our environment become mental representations that are then stored in memory, the current understanding is that “the brain converts your perceptions into chemical and electrical changes that form a mental representation of the patterns you’ve observed” (Brown, Roediger, & McDaniel, 2014, p. 72). How these mental representations became retrievable is illusive, but the process has been referred to as encoding since the early research of Tulving and Thomson (1973), and the representations are called memory traces. Through a process called consolidation, these memory traces are organized and linked to prior knowledge that helps make sense out of the incoming information. Consolidation may take hours to days to occur and is an unconscious phenomenon, resulting in storage in LTM as a schema or mental model. What strengthens the consolidation process is time away from actively thinking about the information, with sleep actually promoting the process.

Retrieval, recalling, or remembering the information is necessary to use the information. Retrieving information from LTM and moving it into working memory “can both strengthen the memory traces and at the same time make them modifiable again, enabling them, for example, to connect to more recent learning. This process is called reconsolidation. This is how retrieval practice modifies and strengthens learning” (Brown et al., 2014, p. 74). Cues, or aspects of the information (content and context), employed to store memories are important for retrieval. These cues are strengthened and new ones added through mental rehearsal, a means of recall or retrieval. Without periodic rehearsal or retrieval, over time the cues are forgotten. The memory remains, but it cannot be accessed and therefore is not retrieved without a cue to bring it forward. Remembering information within a context, however, provides a richer selection of cues that makes information easier to recall. Thus, learning requires encoding into LTM, making associations with multiple cues, and practicing retrieval so that the cues remain active (Brown et al., 2014).

Mental Models

Mental models can range from a single piece of data to a bundle of related knowledge (know that), skills (know how), and a complex set of connections that are difficult to extract, but have to do with applying the information, or knowing when. For example, as a child you might have a schema or mental model for a specific grocery store because your mother takes you to the same store shopping each week. As your experience increases, the concept of grocery store takes on a variety of meanings from a corner convenience store where a small selection of groceries can be purchased to a Walmart Supercenter that has just about everything. Yet, your mental model stored in LTM is that of a grocery store in general that contains multiple links or traces to various types of grocery stores. Mental models or schemas provide bundled storage in LTM, so as not to tax the capacity of working memory or STM. When the data is needed, we can retrieve it via the traces; bringing it back into consciousness for use. If we encounter a new piece of information, such as that grocery stores in Colorado do not sell alcohol, our mental model for grocery stores has just been modified.

Developing mental models takes practice and occurs over time, so it is not surprising that experts have more mental models than novices, which promote the fluid and intuitive performance of the expert. The news is not all good here, as experts often have difficulty deconstructing their mental models because the individual elements are so embedded in complex cognitive structures. Novices, after all, are rule-guided when applying what they have learned, which requires that they learn it in a step-wise fashion (Benner, 1984/2001; Brown et al., 2014).

Cognitive Load

The concept of cognitive load is built on the known limited capacity of working memory. Sweller, Van Merrienboer, and Paas (1998) explained that:

because working memory is most commonly used to process information in the sense of organizing, contrasting, comparing, or working on that information in some manner, humans are probably only able to deal with two or three items of information simultaneously when required to process rather than merely hold information. (p. 252)

Here the authors make a distinction between holding information and actually processing it, which involves, among other activities, that of schema creation or modification. Three types of cognitive load have been identified as intrinsic, extraneous, and germane (Young, Van Merrienboer, Durning, & Ten Cate, 2014).

Intrinsic cognitive load is that imposed by course content itself. For example, if the course content is completely new to students, meaning they have no mental models or prior knowledge to fall back on, and contains multiple interrelated elements, then intrinsic load can be high. Faculty can do little to control for this other than assuring that assigned readings are consistent with the student’s educational level, breaking complex content into smaller more understandable chunks, and choosing reading and assignments strategically.

Extraneous cognitive load is that imposed by the design and organization of learning materials and the online LMS (i.e., Moodle or Blackboard) that faculty can control. The extraneous cognitive load has to do with how the computer–user interface is designed and is discussed in Chapter 12. If the LMS is not well designed, students spend an inordinate amount of time trying to locate information. Also, if the syllabus and organization of the LMS are not parallel, additional extraneous cognitive load is imposed, a topic discussed in Chapter 12.

Two types of extrinsic cognitive load have been identified—split attention and redundancy—which have implications for choosing instructional resources for students. Split attention refers to providing multiple resources for the student to consider. If students’ time is limited, which it often is for nontraditional students, choosing which resource to use, because they do not have time to read or review them all, adds extra cognitive load. Consequently, students’ attention is split among these resources, which may result in students feeling overwhelmed and not attending to any of them. Redundancy refers to providing repetitious resources. This is especially frustrating for students who prefer to make hard copies of references, resulting in wasted paper and printer ink.

Adaptive Memory Framework

Nairne, Thompson, and Pandeirada (2007) formulated a more recent theory of memory termed the adaptive memory framework, which posits that memory is evolutionary and adapts to remember what is most crucial to solving recurring problems related to survival and reproduction. The foundation of their theory is based on how the brain has adapted over time to ensure the survival of the species in a changing environment. Thus, its primary function has been that of remembering important information in order to solve problems.

How does this relate to teaching and learning? Nairne et al. (2007) views memory from a functional perspective as opposed to the structural view of the three-box theory. Their theory posits that we are most attuned to what we care about. This can be translated into our teaching practices by taking the time to determine what information students must know, what they believe is nice to know, and what is really irrelevant to their future goals. Miller (2014) suggests posing these questions to help you make a distinction among these three areas: “Why should students remember the information I’m giving them? Does it relate to their goals? . . . If not, they are likely to forget it—a case of the brain just doing its job the way evolution shaped it to do” (p. 98).


Flavell (1979) first described the term metacognition as “cognition about cognitive phenomena” (p. 906). As cognition refers to thinking, metacognition is really thinking about thinking. Metacognition can be thought of as that internal voice that guides and monitors thinking and learning. I am sure you know what I mean—that little voice in your head that gets your attention to tell you it did not understand what you just read, for example.


Flavel (1979) identified three types of metacognition: knowledge about the self, knowledge about cognitive tasks, and strategic knowledge. Self-knowledge requires knowing one’s strengths and weaknesses to prepare for learning appropriately, being aware of the depth and breadth of one’s knowledge, and understanding what strategies work best for specific learning tasks. Self-knowledge is related to motivation and self-efficacy, which also affect how students approach learning. It is this self-knowledge that leads to the concept of assessment driving learning, for students choose the appropriate study strategy depending on the perceived demands of how the material will be assessed, such as the type of test.

Knowledge about cognitive tasks encompasses the ability to recognize the complexity of a task. This includes matching that task with the appropriate learning strategy and understanding when and why particular strategies are appropriate.

Strategic knowledge is the most applicable in this context as it includes the specific strategies employed for learning and problem solving. Weinstein and Mayer (1983) have grouped these various strategies into five categories: rehearsal, elaboration, organizational, comprehension monitoring, and affective.

Rehearsal is an active, yet not particularly effective, form of learning in which the learner repeats information over and over in an attempt to memorize it. Rehearsal also involves highlighting content in the text or copying important information in notes. Highlighting information in a text to improve learning has been researched by cognitive scientists and found not to be particularly strategic for learning (Roediger, 2013).

Elaboration involves “paraphrasing, summarizing, or describing how new information relates to existing knowledge” (Weinstein & Mayer, 1983, para. 8) and serves to bring forth prior knowledge into working memory to assimilate it with the new information. Elaboration strategies are more effective than rehearsal strategies. Answering self-generated questions or completing those provided in the text is one of the elaboration strategies requiring retrieval from LTM that has been found to be effective for long-term learning and transfer (Brown et al., 2014).

Organizational strategies involve outlining a chapter, concept mapping, or diagraming. These activities serve to bring forth the relationships among and between elements of content (Weinstein & Mayer, 1983).

Comprehension-monitoring activities include those that check for understanding such as reviewing questions in a text prior to reading in order to focus the learner on important content and activate prior knowledge. This type of self-monitoring activity requires that the student set goals, monitor progress in meeting those goals, and modify strategies accordingly (Weinstein & Mayer, 1983).

Affective strategies involve monitoring anxiety levels or negative self-talk of failure to maintain focus on the task at hand and taking active steps to remain alert. Another affective strategy involves studying in a quiet place (Weinstein & Mayer, 1983).


Nursing educators seem to focus on the term reflection, which is often equated with metacognition. Reflective journals and other reflective assignments are common in nursing and do serve as retrieval practice (Brown et al., 2014). The desired outcome of reflection is to improve practice based on a mental review of related knowledge and experience regarding a specific performance. A variety of models exist that include different language, but seem to agree that the purpose of reflection is to identify learning needs; integrate personal beliefs, attitudes, and values with knowledge and experience; and link what is known to new knowledge and experience (Mann, Gordon, & MacLeod, 2009).

Schön (1987), who first described the reflective practitioner, is also credited with separating the construct into reflection-in-action and reflection-on-action. Reflection-in-action is activated when the individual lacks adequate experience performing the task or the task is complex. This form of reflection may be considered parallel to metacognition for it involves thinking about thinking during the performance task to guide thoughts and actions. Reflection-in-action may result in modification of performance based on knowledge and prior experience combined with real-time environmental cues such as a patient’s changing condition. This is difficult for the educator to assess while the performance is occurring, but afterward, the student can verbally walk faculty through what he or she was thinking while performing the task. Conducting research on this construct is equally challenging because what is occurring in the student’s mind cannot be assessed as it is occurring (Mann et al., 2009).

Reflection-on-action is commonly used as an assignment in education as a means to encourage learning from experience. I do think that when the term reflection is used in this context it is this construct of reflection-on-action that is meant. Reflection in this regard occurs after the performance has been completed and requires critical thinking and analysis (Mann et al., 2009). This type of reflection includes revisiting the experience without constraints of time; experiencing the emotions felt during the event or when thinking about the event; and evaluating the experience in terms of how it fits with existing knowledge and experience. The outcome of reflection is to validate, reshape, and/or completely revise future performance (Boud, 2001).

Boud (2001) adds another dimension to reflection-in-action or reflection-on-action and that is reflection-before-action, or preparing oneself for what is to come. He specifies three aspects to this practice: focusing on the learner, the context, and potential learning to be gained. This pre-experience type of reflection brings into conscious awareness what the learner should consider and tune into to gain the most from the learning experience. This strategy is often employed during clinicals when a preceptor takes the student aside prior to performing a new skill to review the procedure, which results in both student and instructor feeling assured that the student is prepared for the task.

Taken together, the work of Schön and Boud was combined into a model of reflective practice by Abrami et al. (2009, as cited in Johnson, 2013) that considers reflection occurring in three stages:

  • Planning before the performance, asking What?

  • Doing during the performance, asking So what?

  • Reflecting after the performance is complete, asking Now what?

I think the questions associated with the three phases are an excellent way to understand the complexity of reflection and to teach all aspects of the construct in a meaningful and useful manner.

Reflective practice is valuable for students, but too often done after the fact to improve future performance, not taking full advantage of the other aspects of reflective practice. Equally important is for students to develop their own metacognitive voices or the ability to reflect-in-action to guide performance as it is occurring. Faculty can promote this process in several ways: (a) by teaching students about reflection-in-action (or metacognition), specifically the value it has in regulating study habits, improving performance, and becoming self-regulated learners; (b) modeling their metacognitive voices by asking probing questions during online discussions or voicing the thought processes they are using to problem solve; and (c) by providing opportunities for reflection-on-action as a means of retrieval practice and self-evaluation (Brown et al., 2014; Schraw, 1998).

Deep and Surface Processing

Two other constructs have an impact on memory formation that involves the approach students take to studying and processing information—surface or deep processing and cognitive load. Studies reported by Beattie, Collins, and McInnes (1997) indicated that students’ approach to learning was dependent on multiple factors, such as the requirements of the task, perceived amount of time to complete the task, overall workload, personal interest or engagement, locus of control or means of motivation (intrinsic vs. extrinsic), level of anxiety, and perceived relevance of the content. We have long known that the requirements of the educational environment, particularly assessment, drive learning (Beattie et al., 1997). Tulving and Thomson (1973) related this phenomenon to encoding specificity. Their research revealed that “encoding of target words was influenced by the list cues present at input and by the subjects’ expectations that they would be tested with those cues” (p. 369).

Students use the surface approach to learning when their goal is memorization. Beattie et al. (1997) describe the surface approach as (a) memorizing without critical evaluation, that is, taking everything at face value; (b) focusing on facts without searching for the underlying concepts and principles; and (c) focusing on passing the test and not long-term learning. If students know they will be tested using knowledge-based multiple-choice questions (MCQs), they will study appropriately, use a surface approach, and memorize the information as unrelated tidbits to be recalled by a cue from a test question.

According to Karpicke and Grimaldi (2012), this surface approach to learning “is thought to produce poorly organized knowledge that lacks coherence and integration, which is reflected in failures to make inferences and transfer knowledge to new problems” (p. 160). Dolmans, Loyens, Marcq, and Gijbels (2015) indicated that the approach to studying is not a stable and consistent trait within students, but most likely a state in response to the requirements of the environment, such as the assessment. The type of assessment and students’ metacognitive self-knowledge allows them to match their preferred study strategies for the type of task, which all too often leads to surface processing to pass a test.

The deep approach to learning is characterized by students making meaning out of what they read and to “relate information to prior knowledge, to structure ideas into comprehensible wholes, and to critically evaluate knowledge and conclusions presented in the text” (Dolmans et al., 2015, para. 4). The deep approach requires (a) studying to learn for understanding using a critical approach, (b) recalling prior knowledge and experience, and (c) carefully comparing presented evidence with arguments and conclusions (Beattie et al., 1997). Both metacognition and reflection are strategies that can promote deep processing.

Specific teaching methods can promote deep learning. Dolmans et al. (2015) reviewed 21 studies on problem-based learning (PBL) that studied the process of PBL with respect to the approach students used to learn—deep or surface. PBL is a case-based approach to learning that requires active, self-directed learning strategies and functions as an assessment as well as a teaching method. Not surprising, 11 of the 21 studies showed that PBL does promote deep learning with no effect on surface learning.

Transfer of Learning

Deep learning provides the foundation for transfer of learning, which is the ability to use information in different contexts, either similar or quite different from how it was learned. Multiple types of transfer have been described in the literature: lateral and vertical, literal and figural, near and far, and specific and nonspecific (Merriam & Leahy, 2005; Mestre, 2005). For our purposes, near and far transfer are most pertinent. Near transfer involves using information in a new but similar situation when compared to how it was learned. Application of what was learned in the classroom or simulation lab to actual care of a patient is a form of near transfer. This involves understanding what was taught instead of simply recalling memorized facts (Glaser, 1991). Far transfer refers to using information learned in one situation to a new and different situation (Detterman & Sternberg, 1993; Haskell, 2001; Mayer, 2002), which is really the goal of higher education (Mayer, 1998). We want our students to learn in such a way that they can take what was learned in school into the workplace in their new role and apply it to various situations and problems.

Mayer (1998) identified three prerequisites for problem-solving transfer: skill, metaskill, and will. Skill refers to the learner’s ability to (a) extract salient content from a lesson, (b) make sense of this information, (c) activate existing associated knowledge, and (d) integrate the new information into what is known in a meaningful way. Metaskill is another term for metacognition and refers to both knowledge and regulation of cognition (thinking). This skill is essential for students to monitor their thinking as well as regulate the process. Will refers to the learner’s motivation for learning that is tied into self-efficacy or belief in his or her own ability to perform successfully. Overall, Mayer’s conceptualization of the requisites for transfer are the most complete with the addition of will, as motivation is essential for both learning and transfer.

Self-Regulated Learning

In order to learn, students must be motivated and channel that motivation into directing their learning (Miller, 2014). Zimmerman (1989) stated that “students can be described as self-regulated to the degree that they are metacognitively, motivationally, and behaviorally active participants in their own learning process” (p. 329). Note that the word active is used here, which is key to the process of self-regulation.

Being metacognitively active refers back to what we discussed in an earlier section, specifically employing strategic metacognition. Strategic metacognitive knowledge encompasses knowing about various learning strategies as well as knowledge of strategies to monitor and regulate learning.

Being motivationally active with regard to self-regulated learning refers to intrinsic motivation or “the inherent interest in and valuing of an activity” (Miller, 2014, p. 168), which depends to some degree on attitudes, level of anxiety, expectancy, and affect (Zimmerman, 1989). Continued motivation is, to some degree, based upon the perceived success from working diligently to meet goals (a behavior), and this self-satisfaction serves to sustain motivation (Zimmerman, 2002).

To be behaviorally active, students must believe in their ability to succeed if they apply themselves, set goals that are achievable, and maintain an interest in achieving the goal, as well as value learning itself (Pintrich, 2002). Being behaviorally active is a product of self-efficacy and study strategies that include effective time management, self-testing, and goal setting.

Multiple Ways of Thinking

For Benner, Sutphen, Leonard, and Day (1984/2010), the term critical thinking was thought to be too restrictive, especially when considering the complexity of today’s clinical nursing practice. They referred to the term critical thinking as a “catch all phrase for the many forms of thinking that nurses use in practice” (p. 84). Additional terms to be considered are clinical reasoning, diagnostic reasoning, clinical reasoning-in-transition, clinical imagination, creative reasoning, scientific reasoning, and format criterial reasoning. The point Benner and colleagues are trying to make, I believe, is that the “multiple ways of thinking” (p. 85) required of nurses differs and is dependent upon the type of nursing practice and as of yet has not been assigned a satisfactory term to adequately describe the complex and inclusive process. Nevertheless, we, as educators, are required to prepare our students for the type of thinking required in practice. To achieve this, we must teach content within the same context in which it will be encountered in the specific nursing roles our students aspire to and are studying for. Doing so will help them build rich mental models that are well cued for easy retrieval when they encounter a similar situation in practice.

So What?

So why is all of this important? The goal of education is transfer. In order for that to occur, deep learning should be promoted using authentic teaching methods so that rich mental models with multiple cues for retrieval are created. Understanding how we learn and the specific concepts related to learning are important for educators, especially when teaching online. If we understand that new mental models are created when the learner encounters unfamiliar information or existing mental models are built upon when newly encountered information adds to or modifies existing knowledge, the value of activating prior knowledge before the learner engages with new material makes sense. Faculty can control cognitive load, to some degree, by strategically choosing teaching methods appropriate to the online environment, being mindful that the complexity of instructional elements matches the learning level of the students, and intuitively organizing the LMS so that it is not in the way of learning. When we model our problem-solving processes during discussions by making our metacognitive thought processes explicit, students will quickly learn the value of metacognition or reflection-in-action and begin to ask themselves and others more probing questions. In addition, instructors should choose assessments that will ultimately drive learning in the desired direction so as to promote deep learning, which will then more readily transfer to the workplace.


The value assessments hold as additional teaching strategies has not been maximized in nursing education, which is largely due to the timing of assessments during a course and the overemphasis on summative assessment. Multiple-choice tests are typically scheduled at midterm and the end of the course to assess learning with little time available for test review, which is a valuable way for students to learn from what they answered correctly (validate learning) or from their mistakes (formative assessment).

Faculty’s perceived need to cover ever-expanding content also impacts time available for test review. This results in faculty setting aside office hours to review tests on an individual student basis, which is not only an inefficient approach, but one that takes even more of faculty’s precious time.

Another concern with reviewing a test with the entire class is test integrity and the ability to reuse test questions in future classes, which leaves faculty with a dilemma as to the best course of action. Although this is a realistic concern, it relates back to the timing of assessments and the overemphasis on one single test—or two if the final test is cumulative—to assess whether students have learned the content. This will be discussed in more detail later in this chapter in the section From Cognitive Science Research. At this juncture, understanding the difference between formative and summative assessments is important in order to begin to grasp how assessments can be used as teaching methods.

Formative Assessment

Formative assessment has been described as assessment for learning or feedback that promotes learning (Kennedy, Chan, Fok, & Yu, 2008). By definition, formative assessment is not connected to termination of an educational activity or a final grade, as it takes place as part of the process of learning (Gikandi, Morrow, & Davis, 2011). Formative assessment comes from feedback from faculty with the goal of helping students improve their work through reflection, reconsideration of their perspectives, and review of educational resources (Sadler, 1989).

Recognizing the need for formative assessment often occurs during small group discussions, for example, when faculty discover that a student is struggling to understand the content and apply it to the question at hand. In other words, faculty recognize that the student has reached his or her zone of proximal development (ZPD), or his or her current ability without assistance, a concept discussed in greater detail in Chapter 2. So, from the learning perspective of formative assessment, faculty have recognized that the student is struggling to learn. From an assessment perspective, faculty have assessed what the student seems to know and understand and has made a mental note of the student’s ability at that particular point in time. By reading the student’s subsequent posts on the ongoing discussion, assessment continues as faculty determine if their intervention was sufficient to promote understanding at the necessary level to successfully complete the task and meet the objectives. Thus, formative assessment has two sides, an assessment side and a teaching side, which occur simultaneously.

Summative Assessment

Summative assessment, an assessment of learning (Kennedy et al., 2008) quantitatively, evaluates performance in the form of a grade that indicates to the learner and other stakeholders at what level the student has learned and met the identified learning outcomes of the assignment (Sadler, 1989). However, summative assessment can also become a means of teaching and learning, if the activity is well placed in the course. By that I mean time exists for continued dialogue with students after they receive a grade. For example, small group discussions are used for formative and summative assessments. If faculty have provided adequate facilitation throughout the discussion, most students will have met the desired learning outcomes.

Another Perspective

Summative assessment is, from my perspective, an unnecessary evil in higher education designed to sort students. Some pass, some fail, but they are all assigned a grade to indicate the level at which they performed. Online discussions are typically graded and become part of the final grade for the course, often encompassing a fair percentage of that grade. After all, they are the main learning space in the course and vital, I might add, to transforming nursing education by addressing the recommendations of Benner and colleagues (1984/2010) to “teach for a sense of salience, situated cognition, and action in particular situations” (p. 82), integrate knowing that with knowing how and knowing when, emphasize multiple ways of thinking, and promote formation into the role. Although small group discussions are likened to classroom discussion, there is really no comparison. Students choose to participate in a classroom discussion, whereas students online must participate, as a fair percentage of their grade—enough to fail the course—is attached to participation that contributes to the ongoing dialogue.

Faculty’s job—and this is important—is to create a discussion topic that is engaging and relevant, and to be present in the discussion to guide learning to meet the desired outcomes. So, my point in all of this is that if we do our job, summative assessment should be fairly straightforward for faculty, and the assigned grade not unexpected for students. Summative assessment would simply involve assigning a grade based on objective criteria in the discussion rubric and commenting on how the student could improve future performance if this is not clear from the rubric itself. Including a few encouraging words is always appreciated by students. Additional comments that pertain to the procedural side of discussions, such as posting on time, replying to the required number of classmates’ posts, and adhering to the word limit on the posts, if not specified in the rubric, are appropriate as they would impact performance in future discussions. I do not see the need for faculty to provide detailed feedback pertaining to a lack of knowledge, understanding, or application that the student can do nothing to remedy. The discussion is over. Consider that if student learning is at the heart of the faculty role, evidence exists that students are engaged and applying themselves in the discussions, and, if adequate feedback and direct instruction have been provided during the discussion, I question the need to deduct points. My concern is that faculty are comparing students’ performance with each other and not against objective criteria. Discussing the difference between norm-referenced and criterion-referenced grading will be useful at this juncture.

Norm-Referenced and Criterion-Referenced Grading

In order for grading to occur, a standard or criteria must be available for comparison against the student’s performance. In days gone by, when high-stakes testing was used for sorting students—to determine those who were suitable for college, those for whom a trade was a good choice, or ordering students to provide a class rank—norm-referenced testing ruled. Student’s performance was compared to other student’s performance, with higher performing students setting the bar. According to Glaser (1963):

Such measures need provide little or no information . . . in terms of what the individual can do. They tell that one student is more or less proficient than another, but do not tell how proficient either of them is with respect to the subject matter tasks involved. (p. 520)

Unfortunately, even today faculty compare one student’s performance to another’s, but in a much less formal and often unconscious way. Well-performing students set the standard for comparison instead of comparing performance against a standard, such as criteria in a rubric.

Criterion-referenced grading, on the other hand, compares student’s performance to a predetermined set of criteria, typically a rubric, where performance is on a continuum from none to desired performance. When a rubric is used for grading, the criteria are objectively stated and the student’s performance compared with those criteria. This serves to keep faculty focused on the criteria and eliminates comparisons of performance with other students (Glaser, 1963).

Because nursing students must master the content (knowing) as well as the skills of using this content in practice (doing) to function in the complexity that is nursing practice, comparing them to another student will not serve them in their future role. Their performance must be assessed as compared to known competencies that are needed for the role. By providing ongoing formative feedback using scaffolding to promote learning, students will be guided toward uncovering what faculty want them to in order to learn deeply and meet the objective of the course. If this happens as the discussion unfolds, no surprises will occur when the discussion has ended and a grade assigned.


The Testing Effect and Spaced Study

One would think that cognitive psychology research pertaining to how people learn would be readily put to practice in schools, but as Pashler, Rohrer, Cepeda, and Carpenter (2007) lament, this is not the case, especially in regard to assessment practices. In particular, testing continues to be timed at midterm and the end of a course as summative assessment when compelling research indicates that frequent quizzes used as formative assessment improve long-term learning, retention, retrieval, and transfer (Roediger & Karpicke, 2006). This phenomenon—termed the testing effect or retrieval practice effect—refers to “the research finding that testing does not just measures [sic] knowledge, but also alters the learning process itself so that new knowledge is retained and transferred more effectively” (Kerfoot et al., 2010, p. 332). Multiple studies have shown that taking a test has a greater effect on future retention and recall of that material than simply studying the material repeatedly or highlighting passages (Roediger, 2013). In effect, test taking affords valuable practice retrieving what was learned and strengthens the process of doing so (Roediger & Karpicke, 2006).

Frequent testing encourages students to engage in spaced study as opposed to massed study. When students know they will be tested weekly, they are more apt to complete the readings and study at the time the content is assigned (spaced study), rather than procrastinating until just before the midterm or final exam (massed study or cramming) (Carpenter, 2012). Kerfoot et al. (2010) define spaced study as “the psychological research finding that information is learned and retained more effectively when presented and repeated during spaced intervals of time (spaced distribution)” (p. 331). Although this seems like a choice students make in their study habits, course design can influence these habits.

The research on spaced study is based on what is termed a study–no study pattern involving study episodes interspersed with time away from studying. In cognitive psychology terms, this means two study episodes are separated by an interstudy interval (ISI), or a period of no study, lasting from a few minutes to days. The second study episode is followed by another no-study period, called the retention interval (RI), which occurs prior to taking the test (Pashler et al., 2007). So, the sequence is basically study, wait, study again, rest, and take the test. A number of studies were done in which the duration of time varied in the ISI and between the RI and test taking to evaluate the process of rather short-term learning, as well as long-term retention of professional skills (Kerfoot, 2009; Kerfoot et al., 2010; Pashler et al., 2007). Overall, these studies focused on the learning of basic facts, skills, concepts, and procedures, or the lower cognitive skills of memorization and understanding. Testing was done with questions that involved recall (retrieval from memory or fill in the blank) or recognition (multiple-choice). The results of a study involving urology residents indicated that spaced study using board preparation-style questions increased retention for up to 2 years (Kerfoot, 2009). Pashler et al. (2007) learned from their research that the positive effects of spaced education on declarative knowledge were as follows:

  • Feedback provided on incorrect answers on a fact-based test afforded a fivefold increase in accurate recall on the final test.

  • Feedback delayed 24 hours improved recall.

  • Matching retrieval practice to how facts will be later retrieved in the workplace improves recall even if the practice of doing so is covert (not verbalized).

  • Testing lessened forgetting when compared to restudying the content.

Kapler, Weston, and Wiseheart (2015) conducted a study with undergraduate students who were divided into two groups. One group reviewed the lecture material 1 day after the lecture and the other group completed their review 8 days after the lecture. Both groups then took a test 5 weeks later that comprised both knowledge- and application-level questions. Students whose study was separated by 8 days scored higher on both levels of questions, underscoring the value of greater spacing of study. Of note is that application-level questions, considered on a higher cognitive level and possibly questions requiring transfer of previously learned concepts to a new situation, were included in the study. This has implications for complex learning that goes beyond remembering or recalling.

For retention of material and long-term learning, the results across the board from various studies indicated that testing just minutes after the second study episode without an RI produced better recall, but poorer long-term retrieval, which perhaps explains why massed study (cramming) may result in an acceptable performance on a test, but faster forgetting. As the goal of teaching is for students to learn deeply in order to recall (retrieve) the information when needed in practice and to be able to think flexibly to transfer what was learned to their future role, spaced study should be encouraged and the benefits of the testing effect employed. The best way to accomplish both is to quiz students frequently, provide correct answers for incorrect responses, and delay feedback on incorrect answers 24 hours. Based on their research, Roediger and Pyc (2012) believed that spaced practice and the testing effect, when used consistently, would certainly improve the learning of basic facts and concepts, providing a foundation for deeper learning in the future. If summative tests are part of the course design, questions should be similar, if not the same, as those on the formative tests.

Variables of the Testing Effect

Type of Test

Roediger and Karpicke (2006) found that the testing effect was stronger with recall (or generative) questions, such as fill in the blank and short answer, compared with MCQs requiring recognition of the right answer. Nevertheless, taking a multiple-choice test did demonstrate improved recall over restudying the material. The authors did caution that the research was not consistent in this regard and additional investigation is warranted with regard to multiple-choice tests. However, given that the software in most LMSs can automatically grade MCQs and provide feedback to students, this type of test is more efficient than fill in the blank or short answer, which faculty must grade by hand. In courses or programs in which summative testing is not used, the research is telling us that frequent multiple-choice tests used as formative assessment will be more effective in promoting retrieval than repeated studying. What also seems promising is that if students can take these tests multiple times to learn the material, their studying will be spaced, which will improve long-term retention.

The Role of Feedback on Tests

Research on feedback is relevant here (Pashler et al., 2007). Surprisingly, immediate feedback on incorrect answers was not superior in promoting retention when compared to delayed feedback—another less-than-intuitive research finding. In fact, delayed feedback for incorrect answers actually improved retention. The reason for this may be related to the spaced study effect of revisiting the content at a later date. Of interest is that without feedback, a phenomenon called the negative suggestion effect can occur with multiple-choice and true–false questions. That is, over time students may recall incorrect answers they gave to test questions and mistakenly think they were the right answers. This issue worsens as more distractors are added to the questions. However, feedback delayed for 24 hours on questions answered incorrectly eliminates this issue (Roediger & Karpicke, 2006).

With regard to quizzes given online, feedback can be easily added to the distractors (incorrect answers) on MCQs in the LMS. To delay feedback, faculty can set a date in the software so that students must wait at least 24 hours after the test closes to review the answers. As online quizzes should be open for at least 5 days so that students can arrange time to take them, this is a reasonable approach that also serves to alleviate potential cheating. If the correct answers are not validated until after the test closes, sharing answers with other students who have yet to take the quiz cannot be done with confidence.

Spaced Education: An Innovation in Teaching

Kerfoot et al. (2010) created an educational strategy called SpacedEd, which was offered online for a number of years and that offered lessons on a wide variety of topics. I had the opportunity to not only participate in one of these modules (cardiac pathology), but also to use a module on basic physical assessment content during an advanced health assessment course with graduate doctor of nursing practice (DNP) students. After signing up for a course, an MCQ was sent via e-mail daily with a link that directed the learner to answer the question online. Feedback was provided for both incorrect and correct answers with links included for remediation or continued study. Each test included approximately 30 to 35 questions. Any question that the learner answered incorrectly was resent intermittently until answered correctly, thus spacing and interleaving the questions. Questions that students answered correctly were also revisited, but at longer intervals. This cycle continued until all of the questions were answered correctly. Kerfoot et al. (2010) conducted a study involving urology residents that compared bolus delivery of questions (all at once) with spaced delivery. Although the bolus delivery resulted in increased recall after 3 months, recall then fell off. However, for the spaced delivery group, retention and recall persisted to 7 months when the study ended.


Interleaving is an instructional strategy that is best explained by comparing it to massed practice, or blocked practice as it is also called. Massed practice involves studying one bit of content over and over until it is mastered (or memorized). Cramming for a test is considered massed practice. Interleaved practice involves mixing up content, but revisiting each part at intervals. Blocked practice would be depicted by AAAABBBBCCCC, and interleaved study by ABCABCABC (Pan, 2015, para. 2). Massed practice often results in improved performance immediately after studying, which is why the results of cramming for a test the next day often pan out. However, this type of study results in rapid forgetting. Interleaving practice seems very counterintuitive and students often find it more effortful. However, it results in better long-term learning than massed practice (Brown et al., 2014).


The original intent of learning-style inventories was for students to understand how they learn best (Fleming & Mills, 1992; Oermann, 2013). Reading the nursing literature on learning styles, the take-away is that knowing students’ preferred learning style is necessary in order to choose appropriate teaching methods to appeal to all learners in the class. Thus, learners learn best when their preferred style is used as the teaching method (Brown et al., 2014). This is an enormous, if not improbable, task for faculty. In the online environment, which is primarily text-based, recorded mini-lectures and podcasts can be used. However, overall teaching methods are limited in this modality.

The topic of learning styles is wrought with controversy. Studies on learning styles seem to be divided into two camps: those who promote educational strategies matching learning styles to maximize learning, and those who feel that deliberately mismatching learning styles will increase the variety of ways in which a student can approach learning (Jeffrey, 2008). The construct of learning styles is imprecise and encompasses, according to Cassidy (2004), “a variety of definitions, theoretical positions, models, interpretations, and measures of the construct” (p. 420). Pashler, McDaniel, Rohrer, and Bjork (2008) reviewed the literature on learning-style inventories and found no well-designed study that demonstrated the validity of these instruments to assess learning styles. They concluded that the widespread use of these inventories in education is simply not supported.


The information presented so far has implication for course design, which is discussed in subsequent chapters. When cases include a problem built into a context applicable to the student’s future role, the student is asked to problem solve—an activity the brain was designed to do from an evolutionary perspective. In order for the student to solve the problem, memorization will not help. Deep processing of information is required as students read, synthesize, and apply what they have read to the case. In addition, the context supports the creation of multiple cues for retrieval, and frequent retrieval deepens learning and creates additional cues for retrieval. By storing the information as a case in cognitive structures, the information has been stored in the same manner in which it will be retrieved when the student encounters a similar problem in the workplace.

Frequent formative testing of the low-stakes variety (few points attached) can serve several purposes—activating prior knowledge, promoting transfer, and strengthening memory traces by practicing retrieval of newly learned information. Test taking is a more efficient way for students to learn compared to spending time rereading and highlighting information. Although this approach seems less than intuitive, surprisingly, students do not object to frequent quizzes, instead finding them beneficial to learning (Roediger & Karpicke, 2006).


Who our learners are, how they learn and at what level, and the strategies available to help them learn more deeply have implications for course design. The terms defined and discussed in this chapter combined with the educational theories and concepts presented in Chapter 2 should provide a sound foundation for the chapters that follow as well as online course design.


  1. American Association of Colleges of Nursing. (2014). Building a framework for the future: Advancing higher education in nursing. Washington, DC: Author.
  2. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. Psychology of Learning and Motivation, 2, 89195.
  3. Beattie, V., Collins, B., & McInnes, B. (1997). Deep and surface learning: A simple or simplistic dichotomy?. Accounting Education, 6(1), 112.
  4. Benner, P. (1984/2001). From novice to expert: Excellence and power in clinical nursing practice. Upper Saddle River, NJ: Prentice Hall Health. (Commemorative edition. Original work published 1984.)
  5. Benner, P., Sutphen, M., Leonard, V., & Day, L. (1984/2010). Educating nurses: A call for radical transformation. San Francisco, CA: Jossey-Bass.
  6. Boud, D. (2001). Using journal writing to enhance reflective practice. New Directions for Adult and Continuing Education, 90, 917.
  7. Brown, P. C., Roediger, H. L., III, & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Cambridge, MA: Belknap Press.
  8. Carpenter, S. K. (2012). Testing enhances the transfer of learning. Current Directions in Psychological Science, 21(5), 279283.
  9. Cassidy, S. (2004). Learning styles: An overview of theories, models, and methods. Educational Psychology, 24(4), 419444.
  10. Chung, E., Turnbull, D., & Chur-Hansen, A. (2014). Who are “non-traditional students”? A systematic review of published definitions in research on mental health or tertiary students. Educational Research and Reviews, 9(23), 12241238.
  11. Detterman, D. K., & Sternberg, R. J. (Eds.). (1993). Transfer on trial: Intelligence, cognition, and instruction. Norwood, NJ: Ablex.
  12. Dolmans, D. H., Loyens, S. M., Marcq, H., & Gijbels, D. (2015). Deep and surface learning in problem-based learning: A review of the literature. Advances in Health Sciences Education, 21(5), 10871112. Retrieved from http://link.springer.com/article/10.1007/s10459-015-9645-6
  13. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906911.
  14. Fleming, N. D., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. Improve the Academy, 11, 137146.
  15. Gikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers & Education, 57, 23332351.
  16. Glaser, R. (1963). Instructional technology and the measurement of learning outcomes: Some questions. American Psychologist, 18(7), 519521.
  17. Glaser, R. (1991). The maturing of the relationship between the science of learning and cognition and educational practice. Learning and Instruction, 1, 129144.
  18. Haskell, R. E. (2001). Transfer of learning: Cognition, instruction, and reasoning. San Diego, CA: Academic Press.
  19. Jeffrey, L. M. (2008). Learning orientations: Diversity in higher education. Learning and Individual Differences, 19, 195208.
  20. Johnson, J. A. (2013). Reflective learning, reflective practice, and metacognition. Journal for Nurses in Professional Development, 29(1), 4648.
  21. Kapler, I. V., Weston, T., & Wiseheart, M. (2015). Spacing in a simulated undergraduate classroom: Long-term benefits for factual and higher-level learning. Learning and Instruction, 36, 3845.
  22. Karpicke, J. D., & Grimaldi, P. J. (2012). Retrieval-based learning: A perspective for enhancing meaningful learning. Educational Psychological Review, 24, 401418.
  23. Kennedy, K. J., Chan, J. K. S., Fok, P. K., & Yu, W. M. (2008). Forms of assessment and their potential for enhancing learning: Conceptual and cultural issues. Educational Research for Policy and Practice, 7(3), 197207.
  24. Kerfoot, B. P. (2009). Learning benefits of on-line spaced education persist for 2 years. Journal of Urology, 181, 26712673.
  25. Kerfoot, B. P., Fu, Y., Baker, H., Connelly, D., Ritchey, M. L., & Genega, E. M. (2010). Online spaced education generates transfer and improves long-term retention of diagnostic skills: A randomized controlled trial. Journal of the American College of Surgeons, 211(3), 331337.
  26. Mann, K., Gordon, J., & MacLeod, A. (2009). Reflection and reflective practice in health professions education: A systematic review. Advances in Health Science Education, 14, 595621.
  27. Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26, 4963.
  28. Mayer, R. E. (2002). Rote versus meaningful learning. Theory Into Practice, 41(4), 226232.
  29. Merriam, S. B., & Leahy, B. (2005). Learning transfer: A review of the research in adult education and training. PAACE Journal of Lifelong Learning, 14, 124.
  30. Mestre, J. P. (Ed.). (2005). Transfer of learning from a modern multidisciplinary perspective. Greenwich, CT: Information Age Publishing.
  31. Miller, M. D. (2014) Minds online: Teaching effectively with technology. Cambridge, MA: Harvard University Press.
  32. Nairne, J. S., Thompson, S. R., & Pandeirada, N. S. (2007). Adaptive memory: Survival processing enhances retention. Journal of Experimental Psycholgy, 33(2), 263273.
  33. National Council of State Boards of Nursing. (2013). National Nursing Workforce Survey. Retrieved from https://www.ncsbn.org/workforce.htm
  34. Oermann, M. H. (2013). Teaching in nursing and role of the educator: The complete guide to practice in teaching, evaluation and curriculum development. New York, NY: Springer Publishing.
  35. Pan, S. C. (2015, August 4). The interleaving effect: Mixing it up boosts learning: Studying related skills or concepts in parallel is a surprisingly effective way to train your brain. Scientific American. Retrieved from http://www.scientificamerican.com/article/the-interleaving-effect-mixing-it-up-boosts-learning
  36. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105119.
  37. Pashler, H., Rohrer, D., Cepeda, N. J., & Carpenter, S. K. (2007). Enhancing learning and retarding forgetting: Choices and consequences. Psychonomic Bulletin & Review, 14(2), 187193.
  38. Pintrich, P. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice, 41(4), 219225.
  39. Radford, A. W., Cominole, M., & Skomsvold, P. (2015). Web tables: Demographic and enrollment characteristics of nontraditional undergraduates, 2011–12. U.S. Department of Education. Retrieved from https://nces.ed.gov/pubs2015/2015025.pdf
  40. Reese, A. C. (1998). Implications of results from cognitive science research for medical education. Medical Education Online, 3(1), 19. Retrieved from http://www.med-ed-online.org/f0000010.htm
  41. Roediger, H. L. (2013). Applying cognitive psychology to education: Translational educational science. Psychological Science in the Public Interest, 14(1) 13.
  42. Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1(3), 181210.
  43. Roediger, H. L., & Pyc, M. A. (2012). Applying cognitive psychology to education: Complexities and prospects. Journal of Applied Research in Memory and Cognition, 1, 263265.
  44. Rosen, L. D. (2011). Teaching the iGeneration. Educational Leadership, 68(5), 1015. Retrieved from http://www.ascd.org/publications/educational-leadership/feb11/vol68/num05/Teaching-the-iGeneration.aspx
  45. Ross-Gordon, J. M. (2011). Research on adult learners: Supporting the needs of a student population that is no longer nontraditional. Peer Review, 13(1). Retrieved from https://www.aacu.org/publications-research/periodicals/research-adult-learners-supporting-needs-student-population-no
  46. Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18, 119144.
  47. Schön, D. (1983). The reflective practitioner. San Francisco, CA: Jossey-Bass.
  48. Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26, 113125.
  49. Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251296.
  50. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80(5), 352373.
  51. Weinstein, C. E., & Mayer, R. E. (1983). The teaching of learning strategies. Innovation Abstracts, 5(32). Retrieved from http://files.eric.ed.gov/fulltext/ED237180.pdf
  52. Wilson, J. L. (2002). Generation X: Who are they? What do they want?. NEA Higher Education Journal. Retrieved from http://www.nea.org/assets/img/PubThoughtAndAction/TAA_98Fal_02.pdf
  53. Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: Implications for medication education: AMEE Guide No. 86. Medical Teacher, 36(5), 371384.
  54. Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 329339.
  55. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 6470.