Finding strengths and solutions: computer-based simulation for experiential learning in social work
Posted June 1, 2022
Bobbi L’Huillier, Allison Nilsen, LCSW, Kate Whitney, LMSW, Maura Lavelle, MS, OTR/L, CHSE, Katie Ondo, MA, CCC-SLP, CHSE
Introduction
The primary objective of learning is to change specific behaviors by acquiring and discovering how to use new information. Content that offers opportunities to apply current comprehension, test knowledge acquisition, and form a connection with the information through storytelling and other engagement techniques all support student learning while fostering retention through active participation. With the advent of computer-based learning, the education field itself has had to identify and implement new approaches to meeting learning demands. As well, multiple social and environmental factors contribute to the need to train students where they are—in traditional classrooms, through online and distance education, synchronously and asynchronously, via real-time communications or using computer-based simulations. Still, the primary objective remains constant: to learn is to change; to achieve competency is to develop these changes into cohesive understanding.
The methods for reaching this understanding continue to evolve. As educators grapple with optimal pathways to reach the most students while maintaining critical standards, computer-based simulation provides a viable, adaptable solution for communicating necessary content and allowing experiential interaction across many professional fields. In the field of social work, for example, educators must develop programs that include specific methods for students to demonstrate knowledge, values, skills, and cognitive and affective processes for behaviors of engagement, assessment, intervention, and evaluation. Meeting these requirements can be challenging under any circumstances, but especially in a field placement setting with limited resources and extremely complex cases (Gursansky & Sueur, 2011; Hunter, Moen, & Raskin, 2015).
Environmental factors also act as a catalyst to spur adoption of new teaching approaches. For example, when the Affordable Care Act passed in 2010, the need for clinical social workers increased (Lundgren & Krull, 2014). In response, practice settings such as the Mount Sinai Hospital Department of Social Work Services began offering professional development to their large social work staff, with administrators implementing an elearning management system and using standardized patient simulations to satisfy the learning requirements of new and experienced social workers (Xanakis, 2018). Academia, too, reflects the problem of demand versus supply: the number of social work programs and, consequently, students who require field placements, far outpaces the availability (Smith, et al., 2021).
More recently, the COVID-19 pandemic has had a significant environmental impact on education, forcing educators to rethink teaching methodologies as well as program requirements, including how best to achieve experiential clinical hours (Tabatabai, 2020). By providing a virtual environment that allows students to practice clinical skills with actual case scenarios, computer-based simulations have emerged in social work research as a complement and potential substitute for some in-person practice experiences (Todd, 2012; Logie, et al., 2013; Bogo, et al., 2017).
The History of Experiential Learning in Social Work
The Council on Social Work Education (CSWE) considers experiential learning as part of field placement the signature pedagogy of social work education (CSWE, 2008) and critical in preparing students for clinical practice (Bogo, 2015). Through direct practice experiences, students endeavor to integrate Kolb’s Experiential Learning Cycle to evaluate concrete experiences they may have in their field placements and work toward reflective observations, abstract conceptualization, and active experimentation (Cheung & Delavega, 2014).
Historically, social work programs have also included role-playing to allow students to demonstrate their basic clinical skills such as interviewing. Limitations of this technique include failure to create a realistic scenario of an agency setting and a lack of sufficient feedback for effective learning. Supplementing role-playing with simulation, however, provides support both for practicing skills and for learning to regulate students’ own emotional responses when working with clients in crisis (Katz, et al., 2014).
In the last decade, social work educators have explored using simulation with standardized clients (actors) as a method for introducing students to real-life case scenarios and to assess practice skills needed in the field (Bogo, et al., 2014; Logie, et al., 2013; Rawlings, 2012). In 2014, CSWE sponsored a Field Summit to review simulation as a possible method for readying students for social work practice (CSWE, 2015). Following the summit, CSWE published the 2015 Educational Policy and Accreditation Standards (EPAS), which states that social work education programs may use simulations as a method for students to accumulate field experience hours. The EPAS standards also called for social work to embrace holistic competence, or “the integration of knowledge, skills, and values[,] and add[ing] the internal cognitive and affective processes of students” (Roberson, 2019, p. 6). As Roberson suggests, with this requirement to introduce students to a broader spectrum of experiential learning, simulation offers a pathway to help reduce the demand versus supply problem and augment field experience while satisfying EPAS standards (2019).
Using Computer-based Simulation to Meet Experiential Learning Needs
Formal research on simulation as an experiential learning technique in education began in the 1960s. Since that time, research indicates simulation provides an active learning approach that interfaces well with current technological advancements to capture students’ attention and “enable them to go beyond the surface understanding of disciplinary content” (Hallinger & Wang, 2020, p. 25).
As the literature supporting simulation use has grown and technological advances have altered access and approaches to educating more students, many disciplines within healthcare, law enforcement, aviation, military, and other settings have implemented computer-based simulation to enhance professional and practical skills (Roberson, 2019). Other allied health professions have also integrated computer-based simulation into curricula, including speech-language pathology (Johnson, et al., 2014), physical therapy (Ghergel, Lavelle, & Ondo, 2019), and occupational therapy (Lavelle, Johnson, & Ondo, 2019).
In the early 2000s, the National Association of Social Workers and the Association of Social Work Boards detailed a framework for using technology in social work in the Standards for Technology and Social Work Practice. The organizations then updated this framework in 2017 in collaboration with the CSWE and the Clinical Social Work Association to provide additional guidance on using virtual reality and computer simulation (Huttar & BrintzenhofeSzoc, 2020). A growing body of subsequent research suggests that in light of the problem of demand outstripping the supply of field placements and educational accessibility in multiple areas, “more asynchronous training will be needed and simulations that are delivered via the internet are highly scalable” (Smith, et al., 2020, p. 194). Statistics support the trend toward online social work programs: as of 2015, 60% of master’s level programs in the United States provided fully online or a hybrid approach for students (CSWE, 2016). With more social work education programs adopting technology to enhance their ability to provide experiential learning, using computer-based simulation to deliver on this need has promising implications as an augmentation and possible alternative to social work field experience.
Support for Computer-based Simulation
For most social work students, their field placement is the first time they serve in the role of a professional social worker. In particular, it is difficult to prepare students for the emotional and psychological impact of working with clients in crisis (Didham, et al., 2011). Computer-based simulation presents a low-risk environment that allows students to develop clinical knowledge while simultaneously reducing the anxiety of making potentially harmful mistakes with clients and offering opportunities to refine skills through repeated practice (Smith, et al., 2020).
As a best practice when using computer-based simulation, students typically receive prebriefing on the clinical setting, client population, and the social worker’s role within the simulation. Students also receive an introduction to the technology they will use and review the expectations of the simulation experience. Within the learning environment, they have different decision choices to make for which they receive immediate feedback. If they find they are making poor choices, students may restart or retake a simulation to improve their performance or to explore the results of using a different approach. Once they complete a simulation, students should discuss their experience, ask questions, and reflect and process personal responses in a debrief with faculty (Stead, Michael, & Ondo, 2022). The debrief component is especially critical for supporting long-term success: as Dreifuerst (2009) states, after experiencing debriefings, students indicate they are more comfortable discussing their personal limitations and feel more confident in their abilities when they work through future simulations. Debriefing has also been shown to improve learner outcomes (Fraser, K., Meguerdichian, M., Haws, J., et al., 2018).
Another benefit to using computer-based simulation is students’ ability to engage with real-life scenarios while receiving scaffolded support from faculty, assistance they may not receive in the field when engaging with clients in need of real-time intervention (Chernikova, et al., 2020). Practicing in a safe, low-stakes holding environment where they can receive constructive feedback helps students build confidence (Dudding, Brown, Estis, et.al, 2019). This support aligns with the do-no-harm mentality of the Social Work Code of Ethics while still providing a learning opportunity for students (Asakura & Bobo, 2021). Computer-based simulation also allows students to engage with clients from varying cultures and provides the opportunity to reflect on these interactions and seek training to provide multicultural care in practice settings (Asakura, et al., 2020).
Additionally, computer-based simulation can help facilitate interprofessional education. Research shows that students who take part in interprofessional simulations better understand their roles within interdisciplinary teams and how to interact on a clinical level with team members from different disciplines (Murphy & Nimmagadda, 2015). Many social work students will go on to share patient care with other professionals throughout their career, so this early experience with a systems perspective allows students to develop best practice strategies that help serve their clients holistically.
Multiple small studies indicate overall satisfaction from most students exposed to computer-based simulation, underscoring the benefit of its use for training professionals. Educators can address concerns such as cost by working with outside resources to ensure robust content. Educators must also consider support for mastering all associated technology as it is critical that students have a thorough understanding of how to use the specific simulation platform to avoid creating an unnecessary barrier to success (Bay, et al., 2021).
While social work may currently lack a large body of research to draw upon as empirical support for computer-based simulation, the field can extrapolate its potential benefit from research into role-playing and in-person simulation, as well as supportive data from computer-based simulation use in other allied health professions. All modalities face some limitations, but computer-based simulations’ adaptable and scalable nature offers inherent flexibility for overcoming many barriers.
The Simucase® Computer-based Platform and Experiential Learning
Social work practice has integrated simulation in academic programs and clinical settings to support experiential learning as a significant component of its core pedagogy. Expanding to employ computer-based simulation seems a logical progression in the field’s efforts to provide training to many students while offering access to a wide patient population. To help meet this need, Simucase develops clinical, real-case simulations in the fields of speech-language pathology, audiology, occupational therapy, physical therapy, and social work that allow students to engage, assess, and apply theory and evidence-based interventions for virtual clients. Throughout its platform, Simucase employs known strengths of computer-based simulations, including a low-risk clinical environment, prebrief and debrief supports, learning objectives mapped to field of practice standards, faculty scaffolding, clients with multiple demographic attributes, the capability for repeated practice to bolster understanding and skills, and interprofessional interactions. Additionally, Simucase provides a solution to factors that historically limit computer-based simulation adoption such as the cost for developing in-house simulation, maintaining user access, faculty training, and performing necessary updates.
For social work, implementing computer-based simulation through Simucase offers advantages that include set costs for services; up-to-date practice environments that correlate with EPAS standards; supplemental activities that encourage critical thinking about theory, application, and self-reflection; and licensed content developers and subject matter experts who strive to enhance simulation experiences. Additionally, Simucase’s proficiency supporting simulation in other healthcare fields helps underscore the efficacy of its training model and its ability to contribute valuable interprofessional content and insight. Finally, Simucase follows best practice in social work field education, identified by Bogo (2015) as having the following components: (a) safe learning environment; (b) collaborative relationships; (c) assessment of competency; (d) multiple practice opportunities; (e) integration of evidence-informed practice; and (f) quality feedback.
Conclusion
Learning is an act of courage: of letting go old beliefs and assimilating new knowledge into action to become stronger, more efficient, and more able to provide useful intervention as needed. With its commitment to helping people identify the need and the pathways to effect change, the field of social work demonstrates deep understanding of learning’s criticality in all aspects of life. Thus, social work educators are embarking on the next phase of training future professionals by adopting computer-based simulation as part of experiential learning.
Research shows the efficacy of simulation in facilitating experiential learning for students in allied health professions. The union between simulation and technology provides an adaptable, scalable method for social work students to develop holistic competence by exploring multidimensional approaches to problem-solving, gaining exposure to a range of clients and interprofessional collaboration, and practicing skill acquisition and demonstration in a nonthreatening environment. By tapping the strengths of this approach, educators in the social work field as well as other professions can continue to innovate solutions that better serve their communities.
References
Asakura, K., & Bogo, M. (2021). Editorial: The use of simulation in advancing clinical social work education and practice. Clinical Social Work Journal, 49(2), 111-116. doi: 10.1007/s10615-021-00810-2
Asakura, K., Bogo, M., Good, B., & Power, R. (2018). Teaching note—Social work serial: Using video-recorded simulation client sessions to teach social work practice. Journal of Social Work Education, 54, 397-404. doi: 10.1080/10437797.2017.1404525
Badger, L. W., & MacNeil, G. (2002). Standardized clients in the classroom: A novel instruction technique for social work educators. Research on Social Work Practice, 12(3), 364-374. doi: 10.1177/1049731502012003002
Bay, U., Maghidman, M., Waugh, J., & Shlonsky, A. (2021). Guidelines for using simulation for online teaching and learning of clinical social work practice in the time of COVID. Clinical Social Work Journal, 49(2), 128-135. doi:10.1007/s10615-021-00807-x
Begun, A. L., Clapp, J. D., & The Alcohol Misuse Grand Challenge Collective. (2016). Reducing and preventing alcohol misuse and its consequences: A grand challenge for social work. The International Journal of Alcohol and Drug Research, 5(2), 73-83. doi: 10.7895/ijadr.v5i2.223
Bogo, M. (2015). Field education for clinical social work practice: Best practices and contemporary challenges. Clinical Social Work Journal, 43, 317-324. doi: 10.1007/s10615-015-0526-5
Bogo, M., Lee, B., Mckee, E., Ramjattan, R., & Baird, S. L. (2017). Bridging class and field: Field instructors’ and liaisons’ reactions to information about students’ baseline performance derived from simulated interviews. Journal of Social Work Education, 53(4), 580-594. doi: 10.1080/10437797.2017.1283269
Bogo, M., Rawlings, M., Katz, C., & Logie, C. (2014). Using simulation in assessment and teaching: OSCE adapted for social work. Alexandria, VA: Council on Social Work Education.
Bogo, M., Regehr, C., Logie, C., Katz, E., Mylopoulos, M., & Regehr, G. (2013). Adapting objective structured clinical examinations to assess social work students’ performance and reflections. Journal of Social Work Education, 47(1), 5-18. doi: 10.2307/23044431
Bragg, J. E., Miller-Cribbs, J., Gordon, J., Gaudet, J., Helman, C, & Munoz, R.T. (2017). Increasing self-efficacy and building hope through simulation-based education. [PDF]. International Journal of Arts & Sciences, 10(2), 549-557. Retrieved February 25, 2022 from http://www.universitypublications.net/ijas/1002/pdf/V7G250.pdf
Chernikova, O., Heitzmann, N., Stadler, M., Seidel, T., & Fischer, F. (2020). Effects of the prior knowledge and scaffolding in facilitating complex skills through simulations: A meta-analysis. In M. Gresalfi and I.S. Horn, (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS), Volume 4 (pp. 2355-2356). Nashville, Tennessee: International Society of the Learning Sciences.
Cheung, M., & Delavega, E. (2014). Five-way experiential learning model for social work education. Social Work Education: The International Journal, 33(8), 1070-1087. doi: 10.1080/02615479.2014.925538
Council on Social Work Education. (2016). Annual Statistics on Social Work Education in the United States. [PDF]. Retrieved March 22, 2002, from https://www.cswe.org/getattachment/992f629c-57cf-4a74-8201- 1db7a6fa4667/2015-Statistics-on-Social-Work-Education.aspx
Council on Social Work Education. (2015). Educational Policy and Accreditation Standards. Alexandria, VA. https://www.cswe.org/
Council on Social Work Education. (2008). The CSWE Handbook of Social Work Accreditation Policies and Procedures (Handbook) for the 2008 Educational Policy and Accreditation Standards (EPAS). [PDF]. Alexandria, VA. https://www.cswe.org/
Didham, S., Dromgole, L., Csiernik, R., Karley, M., & Hurley, D. (2011). Trauma exposure and the social work practicum. Journal of Teaching in Social Work, 31(5), 523-537. doi: 10.1080/08841233.2011.615261
Dudding, C., Brown, D., Estis, J., Szymanski, C., & Szraik, R. (2019). Best practices in healthcare simulations. [PDF]. Council of Academic Programs in Communication Sciences and Disorders, 1-30. Retrieved February 25, 2022, from growthzonesitesprod.azureedge.net/wp-content/uploads/sites/1023/2020/03/ Best-Practices-in-CSD.pdf
Forgey, M. A., Badger, L., Gilbert, T., & Hansen, J. (2013). Using standardized clients to train social workers in intimate partner violence assessment. Journal of Social Work Education, 49(2), 292-306. doi: 10.1080/10437797.2013.768482
Fraser, K., Meguerdichian, M., Haws, J., et al. (2018). Cognitive load theory for debriefing simulations: Implications for faculty development. Advances in Simulation, 28(3), 3-11.
Gelman, C. R. (2004). Anxiety experienced by foundation-year MSW students entering field placement: Implications for admissions, curriculum, and field education. Journal of Social Work Education, 40(1), 39-54.
Gherghel, E., Lavelle, M., & Ondo, K. (2019). Transforming physical therapy education: A look at computer-based simulation. [White Paper]. Simucase Publications. www.simucase.com.
Gursansky, D., & Sueur, E. L. (2011). Conceptualizing field education in the twenty-first century: Contradictions, challenges, and opportunities. Social Work Education, 31(7), 914-931. doi: 10.1080/02615479.2011.595784
Hallinger, P., & Wang, R. (2020). The evolution of simulation-based learning across the disciplines, 1965-2018: A science map of the literature. Simulation & Gaming, 51(1), 9-32. doi: 10.1177/1046878119888246
Hunter, C. A., Moen, J. K., & Raskin, M. S. (Eds.). (2015). Social work field directors: Foundations for excellence. Chicago, IL: Lyceum.
Huttar, C., & BrintzenhofeSzoc, K. (2020). Virtual reality and computer simulation in social work education: A systematic review. Journal of Social Work Education, 56(1), 131-141. doi: 10.1080/10437797.2019.1648221
Johnson, C., Jansen, L., Williams, S., Pantalone, B., & Ondo, K. (2014). Developing a successful simulation-based curriculum. [White Paper.] Simucase Publications. www.simucase.com.
Katz, E., Tufford, L., Bogo, M., & Regehr, C. (2014). Illuminating students’ pre-practicum conceptual and emotional states: Implications for field education. Journal of Teaching in Social Work, 34(1), 96-108. doi: 10.1080/08841233.2013.868391
Lavelle, M., Johnson, C., & Ondo, K. (2019). Enhancing occupational therapy education with clinical simulation. [White Paper.] Simucase Publications. www.simucase.com.
Logie, C., Bogo, M., Regehr, C., & Regehr, G. (2013). A critical appraisal of the use of standardized client simulations in social work education. Journal of Social Work Education, 49, 66-80. doi: 10.1080/10437797.2013.755377
Lundgren, L., & Krull, I. (2014). The Affordable Care Act: New opportunities for social work to take leadership in behavioral health and addiction treatment. Journal of the Society for Social Work and Research, 5(4), 415-438. doi: 10.1086/679302
Mastroleo, N., Humm, L., Williams, C., Kiluk, B., Hoadley, A., & Magill, M. (2020). Initial testing of a computer-based simulation training module to support clinicians’ acquisition of CBT skills for substance use disorder treatment. Journal of Substance Abuse Treatment, 114, 108014. doi: 10.1016/j.jsat.2020.108014
Murphy, J. I., & Nimmagadda, J. (2015) Partnering to provide simulated learning to address interprofessional education collaborative core competencies. Journal of Interprofessional Care, 29(3), 258-259. doi: 10.3109/13561820.2014.942779
Rawlings, M. A. (2012). Assessing BSW student direct practice skill using standardized clients and selfefficacy theory. Journal of Social Work Education, 48(3), 553-576. doi: 10.5175/JSWE.2012.201000070
Roberson, C. J. (2018). Understanding simulation in social work education: A conceptual framework. Journal of Social Work Education, 56(3), 576-586. doi: 10.1080/10437797.2019.1656587
Roberson, C. J. (2019). Simulation as pedagogy: An experiential teaching strategy for social work education. [Banded dissertation]. Retrieved February 25, 2022, from Sophia, the St. Catherine University repository website: https://sophia.stkate.edu/dsw/53
Roulston, A., Cleak, H., & Vreugdenhil, A. (2018). Promoting readiness to practice: Which learning activities promote competence and professional identity for student social workers during practice learning? Journal of Social Work Education, 54(2), 1-15. doi: 10.1080/10437797.2017. 1336140
Sacco, P., Ting, M., Crouch, T., Emery, L., Moreland, M., Bright, C., Frey, J., & DeClemente, C. (2017). SBIRT training in social work education: Evaluating change using standardized patient simulation. Journal of Social Work Practice in the Addictions, 17(1-2), 150-168. doi: 10.1080/1533256X.2017.1302886
Sewell, K. M., Sanders, J. E., Kourgiantakis, T., Katz, E., & Bogo, M. (2020). Cognitive and affective processes: MSW students’ awareness and coping through simulated interviews. Social Work Education, 40(5), 641-655. doi: 10.1080/02615479.2020.1727875
Smith, M., Bornheimer, L., Li, J., Blajeski, S., Hiltz, B., Fischer, D., Check, K., & Ruffolo, M. (2021). Computerized clinical training simulations with virtual clients abusing alcohol: Feasibility, acceptability, and effectiveness. Clinical Social Work Journal, 49(2), 184-196. doi: 10.1007/s10615-020-00779-4
Sowbel, J. E. (2012). Gatekeeping: Why shouldn’t we be ambivalent? Journal of Social Work Education, 48(1), 27-44. doi: 10.5175/JSWE.2012.201000027
Stead, A., Michael, P., & Ondo, K. (2022). Student navigation through computer-based simulations: What predicts success? Teaching and Learning in Communication Sciences and Disorders, 6(1), 10. Retrieved February 25, 2022, from https://ir.library.illinoisstate.edu/tlcsd/vol6/iss1/10/
Sunarich, N., & Rowan, S. (2017). Social work simulation education in the field. [PDF]. Field Educator, 7(1), Simmons School of Social Work. https://www2.simmons.edu/ssw/fe/i/16-130.pdf.
Tabatabai, S. (2020). Simulations and virtual learning supporting clinical education during the Covid-19 pandemic. Advances in Medical Education and Practice, 11, 513-516. doi: 10.2147/AMEP.S257750
Todd, S. (2012). Practicing in the uncertain: Reworking standardized clients as improv theatre. Social Work Education, 31, 302-315. doi: 10.1080/02615479.2011.557427
Tortorelli, C., Choate, P., Clayton, M., El Jamal, N., Kaur, S., & Schantz, K. (2021). Simulation in social work: Creativity of students and faculty during COVID-19. Social Sciences, 10(1), 7. doi: 10.3390/socsci10010007
Vernon, R., Vakalahi, H., Pierce, D., Pittman-Munke, P. & Adkins, L. (2009). Distance education programs in social work: Current and emerging trends. Journal of Social Work Education, 45(2), 263-276. doi: 10.5175/JSWE.2009.200700081
Washburn, M., Parrish, D. E., & Bordnick, P. S. (2017). Virtual patient simulations for brief assessment of mental health disorders in integrated care setting. Social Work in Mental Health, 18(2), 121-148. doi:10.1080/15332985.2017.1336743
Washburn, M., & Zhou, S.(2018). Teaching note—Technology enhanced clinical simulations: Tools for practicing clinical skills in online social work programs. Journal of Social Work Education, 54(2), 1-7. doi:10.1080/10437797.2017.1404519
Wilkins, D., & Jones, R. (2018). Simulating supervision: How do managers respond to a crisis? European Journal of Social Work, 21(3), 454-466. doi: 10.1080/13691457.2017.1366429
Xenakis, N. (2018). Creating a professional development platform to transform social work clinical practice in healthcare. Social Work in Health Care, 57(6), 440-464. doi: 10.1080/00981389.2018.1454373