Neuroscience for Counselors: Recommendations for Developing and Teaching a Graduate Course

Nov 12, 2019 | Articles, Volumes

Deborah L. Duenyas, Chad Luke

 

In recent decades, professional counselors have increasingly focused on neuroscience to inform their case conceptualization and treatment planning with clients. With the additional lens of neuroscience, both the counselor and client can gain new understandings of the client’s issues and improve the quality of the therapeutic relationship. The benefits of integrating neuroscience into the profession of counseling (i.e., neuroscience-informed counseling) are being documented in the scholarly literature; however, information on integrating neuroscience-informed counseling into the counselor education curriculum is sparse. This article describes one teaching approach for a neuroscience-informed counseling course. The structure of the course, methods for effective instruction, and ethical and cultural considerations are discussed.

 

Keywords: neuroscience, counselor education, teaching, neuroscience-informed, instruction

 

 

Neuroscience-informed counseling is a growing force in the counseling profession (Beeson & Field, 2017). The integration of neuroscience into the profession of counseling has been evident over the past two decades. Examples include the development of neuroscience interest networks by the American Counseling Association (ACA), the American Mental Health Counselors Association (AMHCA), and the Association for Counselor Education and Supervision (ACES). There have been numerous books published that focus on neuroscience for counselors (Field, Jones, & Russell-Chapin, 2017; Luke, 2019) and an increased amount of scholarly literature focused on integrating neuroscience into counseling practice (Beeson & Field, 2017; Lorelle & Michel, 2017; Luke, Redekop, & Jones, 2018; Makinson & Young, 2012; Miller, 2016; Myers & Young, 2012).

 

Neuroscience is the study of the brain and nervous system (Kalat, 2019). Neuroscience-informed counseling involves integrating principles from the structure and function of the brain and nervous system to counseling practice (Russell-Chapin, 2016). This integrative work in counseling is being used to treat behavioral and mental health challenges (Field et al., 2017). According to Beeson and Field (2017), neurocounseling is a

 

specialty within the counseling field, defined as the art and science of integrating neuroscience
principles related to the nervous system and physiological processes underlying all human
functioning into the practice of counseling for the purpose of enhancing clinical effectiveness in the
screening and diagnosis of physiological functioning and mental disorders, treatment planning
and delivery, evaluation of outcomes, and wellness promotion. (p. 74)

 

Three methods for integrating neuroscience into the counseling profession have been identified in the scholarly literature, including neuroeducation (Fishbane, 2013), neurofeedback (Myers & Young, 2012), and the use of a metaphor-based approach (Luke, 2016).

 

The first method, neuroeducation, is defined by Miller (2016) as “a didactic or experiential-based intervention that aims to reduce client distress and improve client outcome by helping clients understand the neurological processes underlying mental functioning” (p. 105). Neuroeducation is essentially psychoeducation about the brain and nervous system. Neuroeducation can be used as an intervention to help clients understand the neurological processes that underlie their symptoms and development (Miller, 2016). Miller described various methods for integrating neuroeducation into counseling practice through the use of information on neuroplasticity, brain structures and functions, and memories.

 

     Plasticity is an object’s or organism’s ability to stretch and to be resilient. As applied to the brain and central nervous system, this is called neuroplasticity or neural plasticity, and involves “changes in the activity and connectivity of the various circuits within the nervous system [that] enable learning, encode memory, and drive behavior” (Li, Park, Zhong, & Chen, 2019, p. 44). Information on neuroplasticity and self-defeating patterns of thought and behavior may help demystify change processes.

 

Informing clients about the various brain structures and functions (e.g., brain stem, limbic, and cortical regions) can help with understanding the brain from a developmental perspective—that the brain is built to change and to be resilient (Luke, 2019). Educating clients about how their memories are encoded, stored, and accessed, drawn from the groundbreaking work of Eric Kandel (1976), can help clients gain a better understanding of their own brain and behavior (Miller, 2016). This knowledge can instill hope that although events of the past cannot be changed, the meaning of the memories associated with those events can be changed (Sweatt, 2016). Furthermore, the relational context in which change takes place can help clients’ brains overwrite rigid rules and threats about relationships learned from earlier dysfunctional relationships (Kandel, Dudai, & Mayford, 2014; Schore, 2010; Siegel, 2015).

 

A second method, neurofeedback, has been recognized as an effective treatment for reducing symptoms of various mental health concerns (Russell-Chapin, 2016). A specialized form of biofeedback, neurofeedback changes brain wave patterns to aid in the treatment of conditions such as attention-deficit/hyperactivity disorder, anxiety, depression, addiction, trauma, autism spectrum disorders, and personality disorders (Russell-Chapin, 2016). Neurofeedback is just one method that counselors can use with clients to help them understand and change the function of their brains. Additional examples include basic biofeedback tools and methods like those found on many “smart” watches and fitness trackers.

 

The third method for integrating neuroscience-informed counseling is described by Michael and Luke (2016) as using a metaphor-based approach to teaching the neuroscience of play therapy. This approach is an extension and application of that described in Luke (2016), wherein neuroscience concepts are used both as metaphors for the human experience, as well as understanding brain function. Tay (2017a) has identified the therapeutic value of metaphor and its utility in understanding language and the body. Relatedly, the practices of mindfulness and meditation often use imagery, a form of metaphor, to engage practitioners in engaging more fully in the experience (Tang, Hölzel, & Posner, 2015). As neuroscience-informed counseling continues to become integrated into the work of professional counselors, counselor educators must adapt in order to keep their coursework relevant.

 

Counselor Education and Neuroscience-Informed Counseling

 

Beeson and Field (2017), along with others (Field et al., 2017; Luke, 2017; Miller, 2016) have called for more training for counselors who seek to integrate neuroscience into their practice. They also have identified the challenges associated with infusing neuroscience into counseling courses. The Council for Accreditation of Counseling and Related Educational Programs (CACREP; 2015) standards now require competency in “the biological, neurological, and physiological factors that affect human development, functioning, and behavior” (p. 10). CACREP standards, along with growing momentum in the field, support the development of a course designed specifically for integrating neuroscience for counselors. The AMHCA clinical training standards include recommendations for competence in understanding and applying the biological bases of behavior. The AMHCA standards outline basic knowledge and skills, which include integrating research into practice, as well as clinical interventions.

 

Field et al. (2017) laid a foundation for incorporating neuroscience-informed counseling across the CACREP curriculum. This approach addresses neuroscience in pre-existent courses, yet there is limited availability of literature on how to teach a graduate content course in neuroscience-informed counseling. In the absence of established models for teaching a course in neuroscience-informed counseling, counselor educators and others can feel at a loss for how to proceed. The purpose of this article is to provide recommendations for developing a neuroscience-informed counseling course designed for graduate students. This includes the course structure (e.g., content and resources), methods for effective instruction (e.g., teaching approach and assignments), and ethical considerations.

 

Course Structure: Content and Resources

 

The Neuroscience for Counselors course builds on prior core counseling courses, including counseling theories and the fundamentals of counseling. As such, it represents an extension of counseling theory and fundamentals and is not intended to be a substitute or replacement. Neuroscience-informed counseling explores how different counseling theories and interventions influence and change neurobiology and help facilitate client wellness.

 

The Neuroscience for Counselors course was offered to master’s students enrolled in a CACREP-accredited counseling program at a mid-size university in the northeast region of the United States. The course was offered as an elective that fulfilled three graduate credits toward degree completion. The course was designed as an introduction to neuroscience research and clinical interventions for counselors. Specific attention was given to reviewing the structures, systems, and functions of the brain. Psychodynamic, behavioral, humanistic, and constructivist counseling theories were explored in relation to neuroscience research. The neuroscience of mental health disorders, such as anxiety, depression, stress, and addictions and substance use, were explored.

 

Course assignments included developing a neuroscience-informed guided metaphor; completing a brain resource book on structures, systems, and functions; dyads to practice using neuroscience-informed counseling interventions; reflection in a neuroscience process analysis log (N-PAL); and activities exploring neuroscience-informed technology. A final paper included a case conceptualization based on the 8-factor meta-model (Luke, 2017, 2019) of case conceptualization to explore their client’s presenting concerns.

 

The assigned textbook for this course was Luke’s (2016) Neuroscience for Counselors and Therapists: Integrating the Sciences of Mind and Brain, which focuses on client conceptualization, brain anatomy, various theoretical approaches, and an array of commonly diagnosed mental health concerns. The text also provides case vignettes highlighting how a student might use neuroscience-informed counseling interventions with a diverse population of clients. The first chapter of the text discusses ethical and philosophical issues related to integration. Chapter 2 presents an overview of the basic brain structures, systems, and functions, including neurons and synapses. Chapters 3 through 6 cover the major categories of counseling theories: psychodynamic, cognitive-behavioral, humanistic-existential, and postmodern and constructivist. Chapters 7 through 10 describe conceptualizing and treating anxiety, depression, stress-related disorders, and substance use disorders. The text is written for counselors and counselors-in-training who have little or no background in the physiological bases of behavioral and mental health concerns.

 

     The course instructor provided supplemental material, including magazine articles, peer-reviewed journal publications, apps, videos, websites, and links to neuroscience interest networks. For example, students were provided a link to the Neuroscience News website, which is an independent science news website that offers free cognitive science research papers, neuroscience resources, and a science social network. Also included were links to the Dana Foundation, an organization that supports brain research via grants, publications, and education, and the ACA’s Neurocounseling Interest Network. The supplemental material was selected as a method to broaden student understanding and support knowledge acquisition in neuroscience.

 

Methods: Teaching Approach and Assignments

 

Experiential education is not a new approach in higher education. Educational psychologists in the past, such as John Dewey (1938), Carl Rogers (1969), and David Kolb (1984), have laid the groundwork for the development of contemporary experiential education. Kolb (1984) defined learning as “the process whereby knowledge is created through the transformation of experience. Knowledge results from the combination of grasping and transforming experience” (p. 41). The Association for Experiential Education (AEE; 2019a) defined experiential education as a teaching philosophy “in which educators purposefully engage with learners in direct experience and focused reflection in order to increase knowledge, develop skills, clarify values, and develop people’s capacity to contribute to their communities” (para. 1). In essence, experiential education is the process of learning through experience and reflection.

 

Methods of instruction in the Neuroscience for Counselors course were consistent with the 12 principles of practice outlined by the AEE (2019b). For example, class assignments provided students with the opportunity for reflection, critical thinking, and personal application. The instructor’s teaching roles included “setting suitable experiences, posing problems, setting boundaries, supporting learners, insuring physical and emotional safety, and facilitating the learning process” (AEE, 2019b, para. 9). Sakofs (2001) cautioned that experiential activities can be misused by educators as a form of entertainment with no real educational value. The following six assignments were designed with the intention to deepen students’ understanding of neuroscience concepts as they relate to the profession of counseling.

 

Six Neuroscience Course Assignments

     Developing a neuroscience-informed guided metaphor. Historically, neuroscience has been considered the realm of the medical professional or psychiatrist who has studied the complex inner workings of the brain. Developing a neuroscience-informed guided metaphor provides counseling students the experiential opportunity of taking an unfamiliar concept or idea (i.e., using neuroscience-informed counseling) and making it more accessible by relating it to ideas they are already familiar with (Jamrozik, McQuire, Cardillo, & Chatterjee, 2016; Lawson, 2005). For this assignment, students were assigned to read the article “The Birth of the Neuro-counselor?” (Montes, 2013), in which the term neurocounselor was first used. The article introduces and encourages students to begin thinking about what it means to use neuroscience-informed counseling in practice and how it influences their professional identity as a counselor.

 

After reading the article, students illustrated a guided metaphor that could be used to inform their model of neuroscience-informed counseling practice. Students were provided with the prompt, “Neuroscience-informed counseling is _________” and then asked to fill in the blank with a noun. Students included a paragraph explaining their choice in metaphor and how they came to make that decision. Students were asked to share their metaphors with their peers in class. A student’s illustration could be a visual representation, in writing, or a combination of both. Metaphor is, simply put, the practice of describing one thing in terms of another (Tay, 2017b). More specifically, the use of metaphor increases understanding of a less well-understood concept or idea by describing it in terms of something that is better understood. In the assignment described above, students generated metaphors such as “neuroscience-informed counseling is the first mission to the moon,” “neuroscience-informed counseling is a penlight in a dark maze,” and “neuroscience-informed counseling is a puzzle” to be solved. Lawson (2005) extolled the virtues of metaphors in counseling, noting that they “can help the counselor connect to the client’s world” (p. 135). The use of neuroscience metaphors, whether generated by the client or the counselor, can aid in promoting empathy and therefore trust (Luke, 2017) and can aid in learning neuroscience concepts (Michael & Luke, 2016). For example, in the wildly popular “I Had a Black Dog, His Name Was Depression” World Health Organization video on YouTube (over 9 million views as of this writing), depression is compared to a black dog that affects every facet of an individual’s life (World Health Organization, 2012). The metaphor works by comparing an abstract concept like depression with something concrete like a black dog. It enables the client to experience their depression as something happening to them, not emerging from their core self. When incorporated with relevant neuroscience information, the metaphor takes on increased significance. This black dog hijacks a person’s will, leaving them with diminished options for meaningful action.

 

Developing metaphors for the counselor’s roles when using neuroscience-informed counseling can clarify and strengthen counselor identity. When introducing this assignment, it is important to note that neuroscience-informed counseling is not its own therapeutic orientation. Whereas many graduate counseling programs have courses focused on advanced therapeutic orientations, such as solution-focused therapy or motivational interviewing, a course in neuroscience for counselors can strengthen a counselor’s current theoretical framework (Luke, 2017). For example, counselors practicing cognitive behavior therapy who learn about Hebb’s rule (1949), which states that “neurons that fire together wire together,” along with the concept of neuroplasticity, have another avenue of support for clients working to make positive behavioral changes. In this example, neuroscience can help the client gain awareness of the neurological structures that reinforce their behavior and also provide hard evidence that change is possible (Li et al., 2019). Neuroscience-informed counseling is one of many tools in the counselor toolbox. In addition to conceptualizing neuroscience-informed counseling as part of their professional identity, students also learn content knowledge of the brain’s structures, systems, and functions.

 

     Brain structures, systems, and functions book. This assignment required students to research the basic structures, systems, and functions of the human brain and design their own book. The instructor provided students black and white images of various structures of the brain discussed in the class textbook. Images included lateral and dorsal views of the brain, the two hemispheres of the brain, the three divisions of the brain (i.e., forebrain, midbrain, and hindbrain), the four lobes of the brain (i.e., frontal, temporal, occipital, and parietal), the anatomy of a neuron, and a stem chart of the nervous system tasks, including the sympathetic and parasympathetic nervous system functions. This approach is supported by works such as the Wammes, Meade, and Fernandes (2016) investigation of the neural processes of storing and retrieving memory. The authors found that drawing important words and phrases improves one’s ability to remember important concepts. Students were asked to use various mediums, including colored pencils, crayons, and markers, to label and highlight the different neuroanatomy. Students also were asked to use their class textbook to write descriptions of the functions of these parts of the brain within their assignment.

 

Mental health diagnoses can be intimidating for clients, as can the symptoms of a disorder. Anchoring a client’s experience in their neurobiology can increase their understanding of what is happening. Basic neuroscience information can empower them to learn more about, and in some ways objectify, their experience. In other words, knowledge of the underlying brain function can encourage clients to reflect on mind and body and how they interact. For example, depression is a result of brain function, but the choices an individual makes in response can be a function of the mind. In practice, clients can be led through the process of identifying brain function and mind function.

 

The brain structures, systems, and functions book assignment helps to empower students by providing them with the language and imagery surrounding neuroanatomy. Once counselors feel confident in their knowledge of basic brain regions and systems they can use it to empower clients by providing them a physiological explanation of their experiences. For example, knowledge about the autonomic nervous system can help a client struggling with generalized anxiety disorder. According to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013), generalized anxiety disorder is characterized by excessive anxiety and worry that is difficult to control, with symptoms that might include restlessness, feeling on edge, being easily fatigued, difficulty with concentration, muscle tension, and sleep disruptions. Clients struggling with generalized anxiety disorder can feel as if they are in a constant state of emergency. Understanding how the sympathetic nervous system prepares the body for emergencies can help a client understand what they are experiencing at a physiological level. This can make them more receptive to interventions that activate their parasympathetic nervous system functions and move them from “fight or flight” to “rest and digest.” Once students in the course obtained content knowledge regarding the brain’s structures, systems, and functions, they applied that knowledge in dyads.

 

     Dyads. Experiential learning takes careful planning, structuring of lessons, and intentionality in teaching practices (AEE, 2019). Experiential activities such as dyads can help students learn the material through the act of “doing.” Tollerud and Vernon (2011) described the benefits of experiential learning as “promoting interest in a topic, supporting student retention of the material, and involving students in their education” (p. 285).

 

Luke (2017) outlined neuroscience concepts that can be used as interventions with clients
(e.g., memory systems, Hebb’s rule, left and right brain processing, mirror neurons, attention, and mindfulness). In the neuroscience course, students practiced discussing neuroscience concepts in dyads where they took turns acting as counselor and client. The neuroscience concepts coincided with Chapters 3–10 in the textbook. This provided practice for students using the neuroscience concepts with specific theoretical approaches (e.g., contemporary psychodynamic, behavioral approaches, humanistic approaches, and constructivist approaches), but also could align with a particular mental health diagnosis (e.g., anxiety, depression, stress disorders, and substance use disorders). For example, discussion about Hebb’s rule may apply to counselors working from a behavioral approach or counselors working with clients struggling with specific issues such as substance use.

 

The instructor provided a dyad prompt for students relating to the chapter material for that class session. For instance, the prompt for Chapter 3, Contemporary Psychodynamic Approaches and Neuroscience, was, “Tell me more about your early memories pertaining to key relationships (i.e., parents, siblings, guardians)” and “How do you feel these early memories influence your key relationships today?” The discussion prompt provided the student counselor an avenue to discuss the neuroscience concepts identified in the chapter (i.e., relationships in the brain/interpersonal neurobiology, consciousness, and memory systems) with their mock client. Students were graded on their ability to use the neuro-concepts and attend to their fundamental counseling skills (e.g., unconditional positive regard and empathy).

 

The dyad activities also highlight the positive benefits of right hemisphere to right hemisphere connections validated through neuroscience. According to Badenoch (2008), right hemisphere to right hemisphere connections are at the root of change, as interpersonal connections are rooted in the neural processes of the right hemisphere. Practicing mock counseling sessions provides students the opportunity to develop healthy relationships with their peers in class. This experience can later become a parallel process by which they use the positive experience in class with their future clients.

 

In counseling, two approaches parallel the class experience. In the first, counselors can apply the same material described above with their clients, using process-based psychoeducation. For example, the counselor can present information on the neurobiology and role of early memories, relationships (past and present), and consciousness/unconsciousness in the client’s depression. They can then ask the questions described above directly to the client. The second approach involves a Gestalt technique wherein the client’s depression, their brain, and the client themselves all sit together in the room. The client is guided through a discussion with these constituent parts in order to better understand the role that each plays in the living of the client’s life. As students completed each dyad, a system was created for them to reflect on their experience as described below.

 

     The N-PAL (Neuroscience-Personal Analysis Log). According to Faiver, Brennan, and Britton (2012), the purpose of a personal analysis log (PAL) “is to help students track their progress over the semester in terms of self-awareness and comfort level with the counseling process” (pp. 292–293). Students completed nine neuroscience personal analysis logs (N-PALS) throughout the course. Entries were made in class after each dyad. Students were given the opportunity to analyze and express their feelings in relation to the dyad activities and course material. The purpose of the N-PAL was to help students reflect on their counseling work while integrating neuroscience concepts into the mock counseling sessions with their classmates.

 

N-PALs consisted of five questions: (a) On a scale from 1–10, how confident do you feel applying the assigned theoretical approach for this dyad? (b) On a scale from 1–10, how confident did you feel using neuroscience concepts in this dyad? (c) What were some new areas of growth and development during this dyad? (d) Assess your own performance during this dyad and provide specific examples, and (e) What is your reaction to the course material (i.e., assigned reading, class lecture, videos, discussion)? The N-PAL’s structure is consistent with the experiential education principle, which states that experiences are structured to require the learner to take initiative and make decisions and be accountable for results (AEE, 2019). The questions were developed to encourage students to reflect on their dyadic experiences and think critically about their neuroscience-informed interventions while being held accountable for areas of growth and development.

 

     Exploring neuroscience-informed technology. With the increased focus on neuroscience in popular culture and media, there has been an influx of new neuroscience-informed technology. Students were asked to find three technological tools that could inform their neuroscience-informed clinical work. The tools were to fall into three distinct categories: one app (e.g., mindfulness, anxiety, or brain information app), one video (e.g., YouTube, TedTalk), and one technological application (e.g., pulse oximeter, biofeedback equipment, EEG reader). After identifying the neuroscience-informed technology tools, students posted on an online discussion board describing how they would use their identified tools in a counseling session.

 

There is an abundance of neuroscience-informed technology on the market today. Counselors recommending meditation apps or assorted TedTalks to their clients may be using this technology without awareness of their neuroscientific implications. Counselors do not have to work from memory alone but can take advantage of the growing number of resources available today (e.g., journal articles, books, apps, videos). Counselors who take advantage of resources also must be savvy consumers. For example, prior to recommending apps or videos to clients with neuroscience-related material, counselors should check the source to confirm it is reputable and use the material themselves. Whereas the neuroscience-informed technology discussion post helped to build awareness of technological tools, the final case conceptualization paper served to showcase the content students gained throughout the course.

 

     Case conceptualization. As a summative assignment, students completed a three-part case write-up that demonstrated their ability to conceptualize client issues and apply neuroscience-informed interventions. The instructor provided students with a fictional client case vignette, including biopsychosocial information. The first part of the assignment required students to use an 8-factor meta-model (Luke, 2017, 2019) to conceptualize their client’s case. This 8-factor model is a holistic model identifying eight components that every counselor must consider when working with clients: thoughts, feelings, behaviors, environments, experiences, biology and genetics, relationships, and the socio-cultural context in which the client lives.

 

Students were asked to include neuro-concepts in their discussion of each of the factors. For example, if the student identified that the client was experiencing anxious thoughts, they would include a description of how the amygdala modulates the client’s reactions to events perceived as dangerous or scary. This part of the assignment demonstrated the counseling student’s mastery of case conceptualization in conjunction with their understanding of how neuroscience concepts can influence the client’s symptoms.

 

The second part required students to review their conceptualization and write a phenomenological description of the client across the eight factors of the model. A phenomenological description provides an opportunity for students to consider, beyond the prescribed clinical note, what it might be like to “walk in this client’s shoes.” Writing a phenomenological description uses right-brain processing skills of creativity and intuitiveness. Although the description is the student’s interpretation of the client’s experience, the exercise can strengthen skills in empathic awareness and creative thinking. Thinking about the phenomenology of a client (i.e., what would it be like to walk in the client’s shoes?) can deepen therapeutic rapport, strengthen conceptualization skills, and help build empathy.

 

The third part of the assignment was for students to select a theoretical approach, along with a rationale for their choice, and create a transcript of a session with the client. The transcript had to include a brain-based counseling intervention (e.g., discussion about Hebb’s rule, neuroplasticity, or memory storage). Neuroscience is an essential tool for helping clients understand what is happening to them. For example, a client who has suffered a trauma and is struggling to understand why they cannot remember events clearly may find respite in knowledge regarding how traumatic memories are stored in their brain. Knowledge about neuroscience can help normalize and validate clients’ experiences.

 

In summary, six assignments were described above: neuroscience-informed guided metaphor; brain systems, structures, and functions book; dyads; the N-PAL; exploring neuroscience-informed technology; and a case conceptualization paper. The assignments were developed to build students’ understanding of the material and improve their ability to integrate neuroscience into their case conceptualization, treatment planning, and counseling skills. With the growth of neuroscience integration into the counseling profession, best practice dictates that ethical and cultural considerations are addressed.

 

Ethical Considerations

 

With nascent developments in the counseling profession, such as neuroscience-informed counseling, come potential risks to clients’ well-being. The ACA Code of Ethics (2014) states that “Counselors practice only within the boundaries of their competence, based on their education, training, supervised experience, state and national professional credentials, and appropriate professional experience” (Standard C.2.a). Scholarly literature has recognized the need for professional counselors to work within their scope of practice (Luke, 2019). As the counseling profession continues to integrate neuroscience into practice, the boundaries of that practice are not always clear. For instance, at what level of integration must counselors be educated in neuroscience explicitly? Who governs the practice of integration and ensures that counselors are following best practice, especially when best practice has not been established?

 

Each of the three areas described above—neuroeducation, neurofeedback, and metaphor—present distinct ethical challenges. Neuroeducation, like psychoeducation, can become too didactic and place counselors in the role of content expert, as opposed to process expert. It may be easy for counselors to share brain information with their clients, becoming dependent on sharing facts instead of sharing a process. Studies have demonstrated the potential for harm in the helping relationship when clients view helpers as aloof related to neuro-speak, as clients may feel powerless to change their neurobiology (Kim, Ahn, Johnson, & Knobe, 2016; Lebowitz & Ahn, 2014).

 

Neurofeedback can require advanced knowledge in technological interventions. For example, neurofeedback often requires the use of technological equipment to read and equalize brainwave activity. The Biofeedback Certification International Alliance (n.d.) offers a training program specifically for neurofeedback certification. With certification comes a level of oversight and guidance that promotes proper training of practitioners. However, certification is not a legal requirement to use neurofeedback in counseling practice. Therefore, what is a counselor’s ethical responsibility to acquire education in the use of neurofeedback equipment with clients? How much education is enough to be considered competent? Also, in terms of counselor identity, can neurofeedback be considered counseling or is it an adjunct to counseling?

 

Given these concerns, the use of metaphor may be a reasonable middle ground wherein counselors are still integrating neuroscience into counseling, but not to the extent that it becomes something different. The use of metaphor is less about teaching clients and more about coming to a mutual understanding of the client’s experience using terms that make sense and matter to the client (Tay, 2012). However, this approach requires the counselor to understand brain function and to stay current in the literature to ensure that the metaphor is accurate and apropos to the client situation. For example, memory has been likened to a video recording of events, yet the function of memory has been demonstrated as far more constructed than a recording of facts. In this case, memory is more like a movie wherein the recordings have been edited to tell the story based on the movie-maker’s experience and desire. It is imperative for professional counselors to consider standards of ethical practice in order to meet the ethical principles of beneficence and nonmaleficence. Similarly, counselors also have a responsibility to be aware of cultural considerations when integrating neuroscience into their counseling practice.

 

Cultural Considerations

 

There is a power differential in the therapeutic relationship, in part because of the needs and vulnerabilities that can accompany clients when seeking counseling. Clients might feel disempowered in the counseling relationship because of intersections of race, gender, age, spirituality, and social and economic status (Ratts, Singh, Nassar-McMillan, Butler, & McCullough, 2016). In addition, if counselors use language about the brain that may be perceived as intimidating or unsafe by clients, it could harm the therapeutic relationship. Integrating neuroscience into the counseling profession requires counselors to develop self-awareness surrounding neuroscience terminology and power inequalities in the counseling relationship. It is vital for counselor educators to consider the ethical and cultural implications of teaching a neuroscience-informed counseling course in order to help students learn how to facilitate a therapeutic environment where clients feel safe to process their experiences.

 

Conclusion

 

Given the benefits of neuroscience-informed counseling to treat behavioral and mental health concerns, counselor educators must begin to integrate neuroscience-informed counseling into the curriculum. Developing a neuroscience for counselors course using the aforementioned recommendations for course structure and methods for instruction is one approach to meeting this need. Assignments included a neuroscience-informed guided metaphor; development of a brain structures, systems, and functions book; dyads to practice using neuroscience-informed counseling interventions; N-PALs for reflection; a neuroscience-informed technology discussion post; and a summative case conceptualization paper. Integrating neuroscience-informed counseling into the counseling curriculum, while simultaneously addressing ethical and cultural considerations, has the potential to improve graduate students’ case conceptualizations, treatment planning, and counseling skills.

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

 

 

References

 

American Counseling Association. (2014). ACA code of ethics. Alexandria, VA: Author.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author.

Association for Experiential Education. (2019a). What is experiential education? Retrieved from https://www.aee.org
              /what-is-ee

Association for Experiential Education. (2019b). The principles of practice. Retrieved from https://www.aee.org/what-is-
              ee

Badenoch, B. (2008). Being a brain-wise therapist: A practical guide to interpersonal neurobiology. New York, NY:
W.W. Norton & Co.

Biofeedback Certification International Alliance. (n.d.). Neurofeedback certification. Retrieved from https://www.bcia.org
              /i4a/pages/index.cfm?pageid=3431

Beeson, E. T., & Field, T. A. (2017). Neurocounseling: A new section of the Journal of Mental Health Counseling. Journal of Mental Health Counseling, 39, 71–83. doi:10.17744/mehc.39.1.06

Council for Accreditation of Counseling and Related Educational Programs. (2015). 2016 CACREP standards. Alexandria, VA: Author.

Dewey, J. (1938). Logic: The theory of inquiry. New York, NY: Holt, Rinehart and Winston.

Faiver, C. M., Brennan, M. C., & Britton, P. J. (2012). Field placement: “Where the rubber hits the road.” In D. M. Perera-Diltz & K. C. MacCluskie (Eds.), The counselor educator’s survival guide: Designing and teaching outstanding courses in community mental health counseling and school counseling (pp. 283–297). New York, NY: Routledge.

Field, T. A, Jones, L. K., & Russell-Chapin, L. A. (2017). Neurocounseling: Brain-based clinical approaches. Alexandria, VA: American Counseling Association.

Fishbane, M. D. (2013). Loving with the brain in mind: Neurobiology & couple therapy. New York, NY: W. W. Norton.

Hayes, S. C. (2004). Acceptance and commitment therapy, relational frame theory, and the third wave of behavioral and cognitive therapies. Behavior Therapy, 35, 639–665. doi:10.1016/S0005-7894(04)80013-3

Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York, NY: Wiley.

Jamrozik, A., McQuire, M., Cardillo, E. R., & Chatterjee, A. (2016). Metaphor: Bridging embodiment to abstraction. Psychonomic Bulletin & Review, 23(4), 1080–1089. doi:10.3758/s13423-015-0861-0

Kalat, J. W. (2019). Biological psychology (13th ed.). Belmont, CA: Cengage.

Kandel, E. R. (1976). Cellular basis of behavior: An introduction to behavioral neurobiology. Oxford, England: W. H. Freeman.

Kandel, E. R., Dudai, Y., & Mayford, M. R. (2014). The molecular and systems biology of memory. Cell, 157, 163–186. doi:10.1016/j.cell.2014.03.001

Kim, N. S., Ahn, W.-K., Johnson, S. G. B., & Knobe, J. (2016). The influence of framing on clinicians’ judgments of the biological basis of behaviors. Journal of Experimental Psychology: Applied, 22, 39–47. doi:10.1037/xap0000070

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

Lawson, G. (2005). The hero’s journey as a developmental metaphor in counseling. The Journal of Humanistic Counseling, Education and Development, 44, 134–144. doi:10.1002/j.2164-490X.2005.tb00026.x

Lebowitz, M. S., & Ahn, W.-K. (2014). Effects of biological explanations for mental disorders on clinicians’ empathy. Proceedings of the National Academy of Sciences, 111, 17786–17790. doi:10.1073/pnas.1414058111

Li, J., Park, E., Zhong, L. R., & Chen, L. (2019). Homeostatic synaptic plasticity as a metaplasticity mechanism—
A molecular and cellular perspective. Current Opinion in Neurobiology, 54, 44–53. doi:10.1016/j.conb.2018.08.010

Lorelle, S., & Michel, R. (2017). Neurocounseling: Promoting human growth and development throughout the
life span. Adultspan Journal, 16(2), 106–119. doi:10.1002/adsp.12039

Luke, C. (2016). Neuroscience for counselors and therapists: Integrating the sciences of mind and brain. Thousand Oaks, CA: SAGE.

Luke, C. (2017). Learner-centered counseling theory: An innovative perspective. Journal of Creativity in Mental Health, 12, 305–319. doi:10.1080/15401383.2016.1249445

Luke, C. (2019). Neuroscience for counselors and therapists: Integrating the sciences of mind and brain (2nd ed.). San Diego, CA: Cognella Academic Press.

Luke, C., Redekop, F., & Jones, L. K. (2018). Addiction, stress, and relational disorder: A neuro-informed
approach to intervention. Journal of Mental Health Counseling, 40, 172–186. doi.org/10.17744/mehc.40.2.06

Michael, T., & Luke, C. (2016). Utilizing a metaphoric approach to teach the neuroscience of play therapy: A pilot study. International Journal of Play Therapy, 25, 45–52.

Makinson, R. A., & Young, J. S. (2012). Cognitive behavioral therapy and the treatment of posttraumatic stress disorder: Where counseling and neuroscience meet. Journal of Counseling & Development, 90, 131–140. doi:10.1111/j.1556-6676.2012.00017.x

Miller, R. (2016). Neuroeducation: Integrating brain-based psychoeducation into clinical practice. Journal of Mental Health Counseling, 38(2), 103–115. doi:10.17744/mehc.38.2.02

Montes, S. (2013, November 25). The birth of the neuro-counselor? Counseling Today, 32–40.

Myers, J. E., & Young, J. S. (2012). Brain wave biofeedback: Benefits of integrating neurofeedback in counseling. Journal of Counseling & Development, 90, 20–28. doi:10.1111/j.1556-6676.2012.00003.x

Ratts, M. J., Singh, A. A., Nassar-McMillan, S., Butler, S. K., & McCullough, J. R. (2016). Multicultural and social justice counseling competencies: Guidelines for the counseling profession. Journal of Multicultural Counseling and Development, 44, 28–48. doi:10.1002/jmcd.12035

Rogers, C. R. (1969). Freedom to learn (1st ed.). Columbus, OH: Merrill.

Russell-Chapin, L. A. (2016). Integrating neurocounseling into the counseling profession: An introduction.
Journal of Mental Health Counseling, 38(2), 93–102. doi:10.17744/mehc.38.2.01

Sakofs, M. (2001). I shouldn’t have done it. Next time I won’t. Perspectives. The Journal of Experiential Education, 24, 5–6. doi:10.1177/105382590102400103

Schore, A. N. (2010). The right brain implicit self: A central mechanism of the psychotherapy change process. In J. Petrucelli (Ed.), Knowing, not-knowing and sort-of-knowing: Psychoanalysis and the experience of uncertainty (pp. 177–202). London, England: Karnac.

Siegel, D. J. (2015). The developing mind: How relationships and the brain interact to shape who we are (2nd ed.). New York, NY: Guilford.

Sweatt, J. D. (2016). Neural plasticity and behavior—Sixty years of conceptual advances. Journal of Neurochemistry, 139, 179–199. doi:10.1111/jnc.13580

Tang, Y. Y., Hölzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16, 213–225. doi:10.1038/nrn3916

Tay, D. (2012). Applying the notion of metaphor types to enhance counseling protocols. Journal of Counseling & Development, 90, 142–149. doi:10.1111/j.1556-6676.2012.00019.x

Tay, D. (2017a). Exploring the metaphor–body–psychotherapy relationship. Metaphor and Symbol, 32(3), 178–191. doi:10.1080/10926488.2017.1338021

Tay, D. (2017b). The nuances of metaphor theory for constructivist psychotherapy. Journal of Constructivist
Psychology, 30, 165–181. doi:10.1080/10720537.2016.1161571

Tollerud, T. R., & Vernon, A. (2011). Teaching counseling children and adolescents. In K. Erikson (Ed.), Handbook of counselor preparation: Constructivist, developmental, and experiential approaches (pp. 277–293). Thousand Oaks, CA: SAGE.

Wammes, J. D., Meade, M. E., & Fernandes, M. A. (2016). The drawing effect: Evidence for reliable and robust memory benefits in free recall. The Quarterly Journal of Experimental Psychology, 69, 1752–1776. doi:10.4135/9781452230498.n18

World Health Organization. (2012, October 2). I had a black dog, his name was depression. Retrieved from
https://www.youtube.com/watch?v=XiCrniLQGYc

 

Deborah L. Duenyas is an assistant professor at Kutztown University of Pennsylvania. Chad Luke is an associate professor at Tennessee Technical Institute. Correspondence can be addressed to Deborah Duenyas, OMA Wing – Room 412, P.O. Box 730, Kutztown, PA 19530, duenyas@kutztown.edu.

Class Meeting Schedules in Relation to Students’ Grades and Evaluations of Teaching

Sep 2, 2014 | Articles, Volumes

Robert C. Reardon, Stephen J. Leierer, Donghyuck Lee

A six-year retrospective study of a university career course evaluated the effect of four different class schedule formats on students’ earned grades, expected grades and evaluations of teaching. Some formats exhibited significant differences in earned and expected grades, but significant differences were not observed in student evaluations of instruction. Career services providers, including curriculum designers, administrators and instructors, will find the results of this study helpful in the delivery of services, especially with high-risk freshman students.

Keywords: career, teaching, course, instruction, evaluation, grades

While individual counseling has been shown to be effective in helping students develop career decision-making skills (Brown & Ryan Krane, 2000; Reese & Miller, 2006; Whiston & Oliver, 2005; Whiston, Sexton, & Lasoff, 1998), undergraduate career courses also can be effective interventions (Folsom & Reardon, 2003; Reardon, Folsom, Lee, & Clark, 2011; Whiston et al., 1998).

Although college career courses have been shown to offer substantial benefits (Brown & Ryan Krane, 2000; Osborn, Howard, & Leierer, 2007; Reed, Reardon, Lenz, & Leierer, 2001; Reese & Miller, 2006; Whiston & Oliver, 2005; Whiston et al., 1998), the content and format of such courses vary greatly (Folsom & Reardon, 2003). The present study sought to focus on one aspect of such career course variability: alternative class schedule formats.

Effective career classes can be characterized by these features: (a) structured course approaches appear to be more effective than unstructured approaches (Smith, 1981); (b) individual career exploration should be a cornerstone of the course (Blustein, 1989); and (c) five components (written exercises, individualized interpretations and feedback, in-session occupational exploration, modeling, and building support for choices within one’s social network) (Brown & Ryan Krane, 2000; Brown et al., 2003).

What is the effect that class schedule might have on course effectiveness? Only one study (Vernick, Reardon, & Sampson, 2004) has examined this issue, and the results showed that such courses should be designed to meet more than once a week and avoid over-exposure to materials and activities so as not to overwhelm the student. Extending this concept, we hypothesized that certain course schedule formats (weekly meeting frequency and term length) could make a difference in student learning and evaluation of teaching.

Alternative Career Class Schedules

This study focused on a course based on cognitive information processing theory incorporated into the course textbook, Career Planning and Development: A Comprehensive Approach (Reardon, Lenz, Sampson, & Peterson, 2000). All sections of the course followed a prescribed curriculum comprising a mixture of lectures, panel presentations, small and large group instructional activities, personal research, and field work; however, the classes differed in terms of the class meeting schedule (class duration, number of weekly meetings, and number of weeks a class met during an academic term).

We examined 57 course sections that met over a six-year period and were team-taught by lead instructors and co-instructors with an instructor/student ratio of about 1:8. Lead instructors included both professional staff and faculty who supervised the co-instructors. During the time of this study, four class schedule formats were used. In the case of a 16-week semester, the class met once per week for 3 hours; twice per week for 1.5 hours; or three times weekly for 1 hour. A fourth schedule option was for a 6-week term with the class meeting four times weekly for about 8 hours per week. In the 16-week semester, the class met once per week for 3 hours on Wednesdays (W); twice per week for 1.5 hours on either Monday/Wednesday or Tuesday/Thursday (MW/TuTh); or three times weekly for 1 hour on Monday, Wednesday, and Friday (MWF). A fourth schedule option was for a 6-week term where the class met four times weekly for about 8 hours per week on Monday, Tuesday, Wednesday, and Thursday (MTuWTh). In summary, we sought to evaluate the influence of these four class schedule formats upon the educational experience of the students as measured by expected grades, earned grades, and student evaluations of teaching.

Course Measures

The following section gives details about the three measures of student learning and perceptions of teaching used in this study.

Earned Grade (EG)
Although a student’s grade point average has limitations as a measure of academic achievement, class grades are nevertheless a widely accepted method of quantifying students’ level of educational achievement and future success in graduate school or employment (Plant, Ericsson, Hill, & Asberg, 2005). Specific to career development, Reardon, Leierer, and Lee (2007) showed that grades might be useful measures of career course interventions, “especially if the treatment variables are carefully described and the grading procedures are fully explained and replicable by other researchers” (p. 495). For this study, we assumed that a student’s final EG would accurately reflect learning in the course.

Expected Grade (XG)
Grade expectations are a complex phenomenon that combines realistic data-driven grade expectations with unjustified optimism or wishful thinking (Svanum & Bigatti, 2006). The XG reflects the student’s assessment of course demands and optimism about successfully meeting those demands. This grade prediction may be informed or uninformed; however, after completing multiple assignments over the course of the semester, Svanum and Bigatti (2006) noted that students lower the value of their XG such that it will be only moderately inflated and will reliably predict their final EG. Because students in our course had the course grading scale in the syllabus, a signed performance contract, and predicted their grades during the last week of the semester when 85% of their grade had already been accounted for, we hypothesized that in aggregate their predictions would be only moderately inflated and thus a reliable predictor of their earned grades and success in the course. We felt this grade variable was important as a measure of students’ confidence in their mastery of the career development subject matter and the problem-solving skills taught in the course, and therefore a valid measure of the relative effectiveness of different class schedule formats.

In addition, comparing EG and XG informs us about students’ self-evaluation of learning and their actual performance in the course. When there is not a significant difference between the two scores, we might suppose that students have a fairly accurate understanding of their performance on completed assignments and those still to be graded. By contrast, a significant difference between XG and EG indicates a discrepancy between students’ self-evaluations of graded and as-yet-ungraded assignments and the official final grades. If XG is significantly higher than EG in a section, one may conclude that the academic work has been undervalued by the instructor or overvalued by the students. Conversely, if XG is significantly lower than EG, one might conclude that students’ estimates were conservative or instructors recognized a level of performance not seen by the students.

Student Evaluation of Teaching (SET)
Student evaluation of classes and teaching effectiveness is standard practice at most postsecondary institutions. There is substantial anecdotal and experimental evidence supporting the usefulness of SETs (Centra, 1993; Marsh & Dunkin, 1992; Marsh & Roche, 1997). Certain student ratings forms provide important feedback that can be used to improve teaching performance (Greenwald & Gillmore, 1997; Marsh & Roche, 1997; McKeachie, 1997), and when asked most faculty members support the use of SETs as a tool for teaching improvement (Baxter, 1991; Griffin, 1999; Schmelkin, Spencer, & Gellman, 1997). Although SET is not without its critics, it appears to be a pragmatic way to access and compare student perceptions of teachers’ effectiveness and therefore a potential measure of the relative efficacy of different class schedules.

In an effort to better evaluate students’ course experiences, the influence of EG (Goldman, 1985) and XG (Greenwald & Gillmore, 1997) on SET is receiving considerable attention in the literature. The present study provided an opportunity to examine the relationship of SET to both EG and XG relative to four different class schedule formats.

Research Questions
In seeking to discover if particular class schedules were more effective in a team-taught career course, we evaluated grades and participant feedback from undergraduate students. The goal was to determine if any of the four differing class schedules produced significant differences in the course evaluation measures EG, XG, and SET. Although we were examining these measures from the students’ perspective and such measures are typically scored at the individual student level, we chose to examine class section level scores because XG and SET data were only available to us in this way.

The first group of research questions examined differences between mean evaluative measures, aggregated by class format and averaged for classes that met one (W), two (MW/TuTh), or three times per week (MWF) for 16 weeks, or four times per week (MTuWTh) for 6 weeks.

Research Question 1: Were there any significant differences in the career course evaluation measures among the four class formats?
RQ 1.1: Are there differences in mean EG between formats?
RQ 1.2: Are there differences in mean XG between formats?
RQ 1.3: Are there differences in mean SET between formats?

The second group of research questions explored the differences between the evaluation measures (EG, XG, and SET) within the sections.

Research Question 2: Within any given format, are there significant differences between the mean of the aggregated class evaluation measures?
RQ 2.1: Is the mean XG significantly different than the mean EG?
RQ 2.2: Is the mean XG significantly different than the mean SET?
RQ 2.3: Is the mean EG significantly different than the mean SET?

Method

Participants
Over a 6-year period, 1,479 students were enrolled in 57 sections of a career course to fulfill elective requirements for the baccalaureate degree. The class met in a standard classroom in academic buildings on the campus. Although the class was offered for variable credit, over 95% of the students took it for 3 credit hours. The number of students per section ranged from 19–34 with a mean of 26.5.

Ethnic diversity was generally proportional to the general student population of the university: Caucasian, 74%; African American, 12%; Hispanic American, 7%; Other, 4%; Asian, 3%; and American Indian, .4%. The course typically enrolled about 60% females and 40% males, including freshmen (15%), sophomores (45%), juniors (20%), and seniors (20%). Depending on the semester, between 15% and 25% of the course was composed of students with officially undeclared majors, and the large percentage of sophomores was the result of academic advisors referring these undeclared students to the class. While almost 40% of the members in a typical class reported satisfaction with their present career situation, about 60% were unsure, dissatisfied, or undecided.

Course Grading Procedures
Student grades were computed using scores earned on assignments contained in the performance contract. This contract was comprised of 28 different graded activities spread across the three units of the course. Given the use of the performance contract, students in this course should have had a very good idea of what their final grade would be when they filled out the SET and estimated their grade, because only two of the 28 activities accounting for 125 of 653 total points were still ungraded at that point.

Student Evaluation of Teaching Ratings
We used a standardized instrument for SETs, the Student Instructional Rating System (SIRS; Arreola, 1973), a student course on form developed at Michigan State University (Davis, 1969) and adapted for use at our university. SIRS provided an opportunity for instructors to obtain reactions to their instructional effectiveness and course organization and to compare these results to those of similar courses offered within the university.

The SIRS consisted of 32 items and 25 of these items enabled students to express their degree of satisfaction with the quality of instruction provided in the course by using a 5-point Likert scale. For example, the course was well organized could be marked strongly agree, agree, neutral, disagree, or strongly disagree. One item on the SIRS was of special interest in this study: What grade do you expect to receive in this course? A, B, C, D, or F.

We also employed a second instructional rating instrument, the State University System Student Assessment of Instruction (SUSSAI) which had been used at the university for five years prior to this study. This instrument consisted of eight items focused on class and instructor evaluation. One item was of special interest in this study: Overall assessment of instructor: Excellent=4, Very Good=3, Good=2, Fair=1, Poor=0.

Data Collection
After obtaining permission from the university institutional review board, we received the archived career course grade data for a six-year period. We aggregated the grades of these 1,479 students by class schedule and averaged the results to achieve a mean EG for each class schedule format.

The data relating to students’ perceptions of what they had achieved and the quality of instruction they had received was collected as follows: On the last week of class, while filling out their teacher evaluations, all students in a section were asked to indicate the grade they expected to receive and the results were tallied and averaged to determine a class mean XG. These class averages of 57 sections were forwarded to the researchers, and the results were tallied and averaged to find the mean XG for each class schedule format. In addition, we retrieved overall class ratings of instructors for an ad hoc sample of career classes over the 6-year period. These data enabled us to examine the relationships between mean EG and XG, EG and SET, and XG and SET.

Procedures

In this team-taught course where all instructors were involved in making large- and small-group presentations, each co-instructor had primary responsibility for evaluating the progress of students in his or her small group and assigning a grade, while the lead instructor of the team had overall responsibility for course presentations and management. In completing the SIRS and SUSSAI items for the SET, students were asked to provide a composite rating of the instructional team for their section. SETs were completed anonymously during the final two class meetings while instructors were out of the room and then returned by a student proctor to the university’s office of evaluation services.

Data Analysis
We examined how different class formats influenced mean EG, XG, and SET. The independent variable of class schedule format had four levels. The first three levels met over the course of a 16-week fall or spring semester for either 3 hours once a week (W), 1.5 hours twice a week (MW/TuTh), or 1 hour three times a week (MWF). The final level met for 2 hours four times a week over the course of a 6-week semester (MTuWTh). Because the assumptions related to independence for the three evaluative measures could not be met (i.e., the evaluations for each class section were correlated), we analyzed the data using a split-plot design.

Results

As is the case for other ANOVA and MANOVA tests, the dependent variables were assumed to be normally distributed. We tested the dependent variables to determine if they were normally distributed by computing skewness and kurtosis of each of the dependent variables to see if they fell between −1.0 and +1.0. Both the SET and EG scores did not violate the assumptions of normality as measured by skewness and kurtosis. However, while the skewness of XG did fall within the appropriate range, the kurtosis score was 1.04. Although this score is above 1.00, we believe this minor violation does not seriously affect the results and their interpretation.

Research Question 1
Using the split-plot MANOVA, we found a significant interaction of the three evaluative measures across the four class formats F (6, 106) = 4.47, p < .0005, η2 = .20. Specifically, there was a significant difference in EG between the four course formats, F (3, 53) = 19.15, p < .0005, partial η2 = .52. The EG for schedule MTuWTh (M = 3.50) was significantly higher (p < .005) than that of formats W, MW/TuTh, and MWF (M = 3.25, 3.32, and 3.31, respectively). Next, there was a significant difference in XG between the four course formats, F (3, 53) = 3.62, p = .019, η2 = .02. The means for XG for the W, MW/TuTh, MWF, and MTuWTh were 3.71, 3.57, 3.34, and 3.64, respectively. There was not a significant difference for XG between formats W, MW/TuTh, and MTuWTh. However, there was a significant difference between format MWF and format MTuWTh (p = .036), and format MWF was trending lower when compared with format W (p = .097) and format MW/TuTh (p = .051). Finally, there was not a significant difference on SET scores across the four formats, F(3, 53) = 1.36, p = ns. The mean SET scores for formats W, MW/TuTh, MWF, and MTuWTh were 2.88, 3.15, 3.31, and 3.11, respectively.

Research Question 2
When we compared evaluation measures within each format, we found significant differences with each one, F (2, 52) = 23.61, p < .0005, η2 = .47. We found XG significantly greater than EG within schedule format W (.46, p = .002) and format MW/TuTh (.35, p < .0005). By contrast, the difference between XG and EG was smaller and not statistically significant within format MWF (.13, p = ns) and format MTuWTh (.13, p = ns). This lack of a significant difference between EG and XG indicates that these students earned grades very similar to the grades they expected to receive. It is apparent that the students and instructors used similar evaluation and grading methods. Stated another way, this finding suggests that students in classes meeting more frequently per week have a slightly more accurate perception of how they are doing in the class.

We also found that mean XG was significantly greater than mean SET for format W (.83, p =.003), format MW/TuTh (.42, p < .0005), and format MTuWTh (.53, p < .0005). However, there was not a significant difference between XG and SET for format MWF (.13, p = ns). Finally, in comparing the difference between mean EG and mean SET within each of the four formats, we found a significantly higher EG only for format MTuWTh (.40, p < .0005). No significant differences were observed for formats W, MW/TuTh, and MWF, which had differences of .37, .07, and .13, respectively.

In summary, we found significant differences in the evaluation measures of XG, EG, and SET across the four different career course formats. Class sections which met four times a week for 6 weeks had a significantly higher EG than classes meeting one, two, or three times a week for a 16-week semester. Interestingly, formats W, MW/TuTh, and MTuWTh all had mean XG scores over 3.55, while format MWF’s XG was not only lower than the other formats, but significantly lower than that of format MTuWTh. Finally, mean SET scores were not significantly different from one another. Notably, they were all well above the rating of “good” (good = 2.0), with a mean of 3.15 on a 4-point scale. Means for the sections ranged between 2.88 and 3.31; thus we concluded that students found the instruction to be very good or excellent.

Discussion

Career course interventions have been developed to help students improve their academic and career decision-making skills. Comprehensive career courses offered for academic credit represent a cost-effective intervention that could be described as a “mega-dose” of career services (Reardon et al., 2011). While the benefits of college career courses are clear, it is unclear what contributions specific class formats (differing by length of class period, number of classes per week, length of course in weeks) might make to their effectiveness. Thus, the purpose of our study was to analyze the influence of different schedule formats on earned and expected grades and students’ evaluation of their instructors.

Previous studies on career development classes have described various limitations (see Gold, Kivlighan, Kerr, & Kramer, 1993; Reese & Miller, 2010), and we attempted to address these in the following ways. First, although we did not directly address random selection and random assignment issues, we aggregated class section scores instead of individual student scores, thus reducing the effect of individual outliers. By using the aggregate mean for each career planning section, individual students’ evaluation of the teacher remained anonymous yet the evaluation of the course section remained intact. The second limitation described by other researchers is the small number of participants in the career class analyzed. Over a six-year period we were able to collect data from almost 1,500 students from 57 sections of the course. The third limitation we attempted to address was the lack of equal representation of different ethnic groups. While we did not have equal percentages of students from different ethnicities, the demographic composition of our sample closely matched the composition of our university.

Perhaps the greatest strength of this study’s design was the replication of the intervention. That is, because the course structure and specific assignments were very similar for all sections, in effect the replication of the career course occurred across all 57 of the course sections analyzed. In each section, the course content and procedures were clearly specified and grades were based on the successful execution of a performance contract by the student.

Earned and Expected Grades
We examined how schedule influenced mean earned grade (EG) and expected grade (XG) scores. Like Vernick et al. (2004), we found that sections meeting only once per week over 16 weeks (format W) had the lowest EG, though not significantly lower than formats MW/TuTh and MWF. By contrast, schedule MTuWTh had a significantly higher EG than all the other formats, suggesting that a 6-week semester of 2-hour class meetings four times a week was more conducive to learning than a 16-week semester of classes meeting one, two, or three times per week for 3 hours, 1.5 hours, or 1 hour, respectively; that is, the “mega-dose” of career development interventions given in the course were intensified with MTuWTh.

Further analysis of the difference between mean section EG and XG scores enables us to compare the students’ view of their performance in the course with their actual performance. Ideally, we would prefer that there not be a significant difference between XG and EG in order to increase students’ confidence about the fairness of the grading and their sense of having mastered the material in the course. Expanding on these points, when the section mean XG was significantly higher than the mean EG, students could have left the course with a sense of failure and disappointment. Interestingly, in this study schedules W and MW/TuTh had significantly higher mean XG than mean EG, indicating an incongruity between the expected and earned grades. By contrast, for both schedules MWF and MTWF, the difference between mean XG and mean EG was not significant. One might conclude that fewer course meetings per week increased the difference between XG and EG scores.

Student Evaluation of Teaching
With regard to SET, there were no significant differences between the four class schedule formats, although we had suspected this might be the case. Perhaps a significant difference between section means for SET and XG would describe an incongruity between the students’ estimate of instruction quality and their evaluation of their own performance in the course. If XG were significantly higher than SET, this finding might indicate that students in these sections believed their performance was more related to their abilities and efforts rather than course instruction. By contrast, sections with significantly lower XG than SET scores may have rated instructors’ presentation of material higher than their own performance in the course. Interestingly, for schedules W, MW/TuTh, and MTuWTh, XG was significantly higher than SET, suggesting that students evaluated themselves more favorably than they did their instructors. We found it curious that for schedule MWF alone, XG was not significantly higher than SET.

Finally, EG is assigned to the student by the instructor, while SET is assigned to the instructor by the student. By comparing mean EG with SET, we can examine the relationship between an instructor’s evaluation of his or her students with students’ evaluation of the instructor. When EG is greater than SET, this means that instructors evaluated their students more favorably than they themselves were evaluated; conversely, when SET is greater than EG, students evaluated instructors more favorably than they themselves were evaluated. For schedules W, MW/TuTh, and MWF, there were no significant differences between mean EG and SET scores. However, for MTuWTh, in which students achieved a significantly higher mean EG than the other formats, the EG also was significantly higher than the SET, suggesting that this high-performing group had higher expectations for their instructors than they felt the instructors met.

Limitations

Because this study is field research, there are a few limitations to discuss. First, participants were undergraduates taking a career planning course from one university. The advantage to using this approach was consistency of teaching content, training and quality control of teaching personnel, administration of tests, and assignments, thus reducing the possibility that course differences were responsible for random error variance. But, because these results come from only one university’s career course, caution should be exercised when generalizing them to other courses.

Second, participants were not randomly selected. In fact, random assignment was impossible given the students’ autonomy in selecting this course. Random selection is seldom an option in field research at an educational institution, but this fact does restrict the robustness and generalizability of results to other populations (Babbie, 2001).

Third, participants in the study may have been experiencing more career-related difficulties than other students who did not elect to take the course. It is to be expected that participants perceived a career course as more important to their progress than nonparticipants, which limits generalizability of these findings (Smith & Glass, 1987).

Fourth, because the data were collected over a six-year period, it is difficult to determine the effect of historical events on the behavior and attitudes of participants (Smith & Glass, 1987; Van Dalen, 1979). For example, students from the initial semester of the study took the class at the height of the tech bubble, while others took the class in the shadows of the 9-11 tragedy. Although we were not able to control for these events, we acknowledge that researchers and practitioners must be aware of the influence of external events upon any college course.

Implications

There are several implications regarding the findings of this study. The significant differences found between schedule formats in the outcomes of EG and XG serve to remind instructors, those who supervise them, and those managing career courses about the potential impact of this variable. For example, these findings indicate that classes meeting one time per week for three hours are not characterized by higher earned grades, and by implication this means student learning. Additional studies should isolate and evaluate format variables such as length of the entire course, number of classes per week, and length of individual classes so that those evaluating teachers might consider this in their evaluations. At the same time, the absence of any differences in student evaluations of teaching across the four schedule formats is reassuring for those teaching and supervising instructors, at least in a course that was as highly structured and standardized as the one in this study.

Career services providers, curriculum designers, administrators, and instructors may wish to consider these findings when making decisions about the design and delivery of career courses, especially for high-risk freshmen (Osborn et al., 2007). Students meeting for four classes a week over a 6-week semester earned and expected significantly higher grades overall than students meeting over a 16-week semester. Taking the 6-week intensive course during the summer term before beginning the freshman year could both increase students’ chances of academic success and their confidence in navigating the college experience.

References

Arreola, R. A. (1973). A cross-institutional factor structure replication of the Michigan State University SIRS faculty evaluation model. College Student Journal, 7, 38–42.
Babbie, E. (2001). The practice of social research. Belmont, CA: Wadsworth/Thomson Learning.
Baxter, E. P. (1991). The TEVAL experience, 1983–88: The impact of a student evaluation of teaching scheme on university teachers. Studies in Higher Education, 16, 151–179.
Blustein, D. L. (1989). The role of career exploration in the career decision making of college students. Journal of College Student Development, 30, 111–117.
Brown, S. D., & Ryan Krane, N. E. (2000). Four (or five) sessions and a cloud of dust: Old assumptions and new observations about career counseling. In S. D. Brown & R. W. Lent (Eds.), Handbook of counseling psychology (3rd ed., pp. 740–766). New York, NY: John Wiley & Sons.
Brown, S. D., Ryan Krane, N. E., Brecheisen, J., Castelino, P., Budisin, I., Miller, M., & Edens, L. (2003). Critical ingredients of career choice interventions: More analyses and new hypotheses. Journal of Vocational Behavior, 62, 411–428.
Centra, J. A. (1993). Reflective faculty evaluation: Enhancing teaching and determining faculty effectiveness. San Francisco, CA: Jossey-Bass.
Davis, R. H. (1969). Student Instructional Rating System (SIRS) Technical Bulletin. East Lansing, MI: Michigan State University, Office of Evaluation Services.
Folsom, B., & Reardon, R. (2003). College career courses: Design and accountability. Journal of Career Assessment, 11, 421–450.
Gold, P. B., Kivlighan, D. M. Jr., Kerr, A. E., & Kramer, L. A. (1993).The structure of students’ perceptions of impactful, helpful events in career exploration classes. Journal of Career Assessment, 1, 145–161.
Goldman, L. (1985). The betrayal of gatekeepers: Grade inflation. Journal of General Education, 37, 97–121.
Greenwald, A. G., & Gillmore, G. M. (1997). Grading leniency is a removable contaminant of student ratings. American Psychologist, 52, 1209–1217.
Griffin, B. W. (1999). Results of the faculty survey on student ratings of instruction: Preliminary report. Statesboro, GA: Georgia Southern University, Student Ratings Committee.
Marsh, H. W., & Dunkin, M. (1992). Students’ evaluations of university teaching: A multidimensional perspective. In J. C. Smart (Ed.), Higher education: Handbook on theory and research (Vol. 8, pp. 143–234). New York, NY: Agathon Press.
Marsh, H. W., & Roche, L. A. (1997). Making students’ evaluations of teaching effectiveness effective: The critical issues of validity, bias, and utility. American Psychologist, 52, 1187–1197.
McKeachie, W. J. (1997). Student ratings: The validity of use. American Psychologist, 52, 1218–1225.
Osborn, D. S., Howard, D. K., & Leierer, S. J. (2007).The effect of a career development course on the dysfunctional career thoughts of racially and ethnically diverse college freshmen. Career Development Quarterly, 55, 365–377.
Plant, E. A., Ericsson, K. A., Hill, L., & Asberg, K. (2005). Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemporary Educational Psychology 30, 96–116. doi:10.1016/j.cedpsych.2004.06.001
Reardon, R. C., Folsom, B., Lee, D., & Clark, J. (2011). The effects of college career courses on learner outputs & outcomes: Technical report No. 531. Tallahassee, FL: Center for the Study of Technology in Counseling and Career Development, Florida State University. Retrieved from http://career.fsu.edu/techcenter/whatsnew/TechRept53.pdf.
Reardon, R. C., Leierer, S. J., & Lee, D. (2007). Charting grades over 26 years to evaluate a career course. Journal of Career Assessment, 15, 483–498. doi:10.1177/1069072707305767.
Reardon, R. C., Lenz, J. G., Sampson, J. P., Jr., & Peterson, G. W. (2000). Career development and planning: A comprehensive approach. Pacific Grove, CA: Wadsworth-Brooks/Cole.
Reed, C., Reardon, R., Lenz, J., & Leierer, S. (2001). Reducing negative career thoughts with a career course. Career Development Quarterly, 50, 158–167.
Reese, R. J., & Miller, C. D. (2006). Effects of a university career development course on career decision-making self-efficacy. Journal of Career Assessment, 14, 252–26.
Reese, R. J., & Miller, C. D. (2010). Using outcome to improve a career development course: Closing the scientist-practitioner gap. Journal of Career Assessment, 18, 207–219.
Schmelkin, L. P., Spencer, K. J., & Gellman, E. S. (1997). Faculty perspectives on course and teacher evaluations. Research in Higher Education, 38, 575–592.
Smith, G. E. (1981). The effectiveness of a career guidance class: An organizational comparison. Journal of College Student Personnel, 22, 120–124.
Smith, M. L., & Glass, G. (1987). Research and evaluation in education and the social sciences. Englewood Cliffs, NJ: Prentice-Hall.
Svanum, S., & Bigatti, S. (2006). Grade expectations: Informed or uninformed optimism, or both? Teaching of Psychology, 33, 14–18.
Van Dalen, D. (1979). Understanding educational research: An introduction. New York, NY: McGraw-Hill.
Vernick, S. H., Reardon, R. C., & Sampson, J. P. Jr. (2004). Process evaluation of a career course: A replication and extension. Journal of Career Development, 30, 201–213.
Whiston, S. C., Sexton, T. L., & Lasoff, D. L. (1998). Career-intervention outcome: A replication and extension of Oliver and Spokane. Journal of Counseling Psychology, 45, 150–165.
Whiston, S. C., & Oliver, L. W. (2005). Career counseling process and outcome. In W. B. Walsh & M. Savickas (Eds.), Handbook of vocational psychology (3rd ed., pp. 155–194). Hillsdale, NJ: Erlbaum.

Robert C. Reardon, NCC, NCCC, is Professor Emeritus at the Career Center at Florida State University. Stephen J. Leierer is an Associate Professor at East Carolina University. Donghyuck Lee is an Assistant Professor at Konkuk University in Seoul, Korea. Correspondence can be addressed to Robert C. Reardon, Career Center, Florida State University, 100 S. Woodward St., Tallahassee, FL 32306-4162, rreardon@admin.fsu.edu.