Teaching Students How To Learn

Download the HANDBOOK, Helping Instructors Teach Students “How to Learn” directly and at any time using this link.

About the HANDBOOK

Written by Stephanie N. Del Tufo, Assistant Professor of Education and the Interdisciplinary Neuroscience Graduate Program; and Chrystalla Mouza, School of Education Director and Distinguished Professor of Teacher Education; with contributions from Matthew Trevett-Smith, Center for Teaching & Assessment of Learning Director; Rose Muravchick, CTAL Associate Director for Educational Development; and Stacie Larkin, CTAL Faculty Developer.

This handbook provides a brief introduction to the idea of helping our UD students learn how to be better learners. It also provides clear guidance on how instructors (regardless of their discipline) can introduce explicit learning outcomes into their curriculum/courses along with concrete assignment examples and feedback techniques. Instructors are encouraged to schedule a CTAL consultation with one of our staff members as you leverage this exciting new resource.

How to reference this handbook: Del Tufo, S.N., Mouza, C., Trevett-Smith, M., Muravchick, R., & Larkin, S. (2021). Helping instructors teach students how to learn: a Center for Teaching & Assessment of Learning handbook. http://www.udel.edu/008466

Learning How to Learn Outcomes

The foundational learning outcomes developed for this handbook are broad, discipline-agnostic outcomes that individual faculty can use to refine Specific Learning Outcomes (SLOs) tailored to their specific pedagogical style, program goals, and disciplinary expectations. Upon successful completion of a course with these foundational learning outcomes, students will be able to:

  1. Explain the relationship between key components of self-regulated learning.
  2. Enact strategies that lead to the development of learning-how-to-learn competencies across all three phases: forethought, performance, and self-reflection.
  3. Evaluate the quality of information and/or ideas through feedback, including the ability to analyze peers’ work, apply feedback to improve the quality of work, and develop a greater conceptual understanding of the material at hand.
  4. Demonstrate understanding of seeking information from a variety of sources (textbook, Internet, etc.), including the ability to evaluate the accuracy and credibility of sources.

This HANDBOOK provides explicit guidelines and steps to support instructors as they incorporate any of the four student learning outcomes on learning-how-to-learn identified above. In each step, you will find a rationale as well as specific guiding questions for implementation. 

Why Learning How to Learn Matters for Students

The transition from high school to college is challenging for many students because it requires that they become more independent learners (van der Meer et al., 2010). First-generation students and those from underrepresented backgrounds, in particular, are less prepared for college compared to peers coming from families with college degrees. Further, they are more likely to experience challenges adapting to college because of sociocultural differences in the norms, values, and expectations between their home environments and college campuses (Stephens et al., 2012). As students engage with college work they need to learn how to manage their time, develop effective study skills, optimize their study environments, and monitor their learning – in other words, they must learn how to learn. These skills are generally captured under a conceptual framework known as self-regulated learning. Self-regulated learning refers to the manner in which learners achieve their personal learning goals by systematically activating and sustaining their cognition, motivation, behaviors, and emotions (Schunk & Greene, 2018). It includes three distinct phases focusing on processes prior to learning (planning), during learning (progress monitoring), and after learning (self-reflection) (Zimmerman, 2002).

How Instructors Can Help Studens Learn How to Learn

Self-regulated learners exhibit characteristics of learning how to learn: “They are able to pursue valued academic goals via planning, monitoring, controlling, and evaluating their cognition, motivation, behavior, and emotions, such as by using effective strategies and knowing how to self-motivate” (Greene et al., 2020, p. 949). Research has established a clear connection between self-regulation and academic performance (e.g., Dent & Koenka, 2016). Importantly, the knowledge, skills, and dispositions needed to enact self-regulated learning are not innate but can be learned (Bjork et al., 2013). In fact, all students can learn how to self-regulate independent of age, gender, motivation, and other individual characteristics (Pintrich, 1995). As a result, instructors need to help students become self-regulated learners by incorporating strategies across courses and contexts and providing students with practice opportunities (Pintrich, 1995).

Why This Strategy Works

Self-regulated learning interventions for college students have revealed positive outcomes. Greene et al. (2020) investigated the impact of a first-year semester-long course focusing on self-regulated learning on first-generation college students. The course provided direct instruction on self-regulated learning and strategies as well as opportunities in class to practice various aspects of self-regulated learning during course activities. Improvement in conceptual knowledge was seen even when students only received access to the course materials. Further, the course helped students acquire monitoring and strategy-use knowledge that could be applied across contexts. Instead of using a semester-long course, Bernacki et al. (2019) examined whether brief, digital training modules aimed at helping students apply learning strategies and self-regulated learning principles in STEM courses can impact students’ behaviors as well as performance in a large biology course. Results indicated that even a short digital training module on “learning-how-to-learn” had a positive impact on students’ use of resources for planning, monitoring, and strategy use. Further, the training helped improve students’ scores on content area quizzes and exams. Similar learning-to-learn programs were implemented at various institutions both in the U.S. and abroad with positive outcomes for students, including first-generation students and those on academic probation (e.g., Bowering et al., 2017). In other words, teaching students “how to learn” using evidence-based best practices works.

Learning Myths, Misnomers, & Misconceptions

Myth: First-Year Post-Secondary Students Are Independent Learners

The assumption that first-year college students can be autonomous and independent learners is demonstrably false (e.g., Christie et al., 2013; Noyens et al., 2017; Räisänen et al., 2016). While researchers actually agree on the importance of self-regulated learning skills in higher education, lower education, and beyond education, we often forget that university students did not arrive on campus with these skills (e.g., Bjork et al., 2013; Nugent et al., 2019). The vast majority of our students require educator co-regulation to learn effectively (Räisänen et al., 2016). Co-regulation requires that we, as instructors, gradually shift self-regulatory ownership to the learner (i.e., gradual release of responsibility). It is important to remember that retaining control over self-regulation causes “destructive friction” for students (Vermunt & Verloop, 1999), resulting in decreased learning and thinking when instructions are too prescriptive (Vermunt, 2006). Put simply, students must be taught to become independent learners

Learning Misnomers

To use proper learning strategies, learners must first understand what those strategies are and what they are not. Thus far, proper learning strategies have been the sole focus of this handbook. Here, we aim to dispel some myths that appear to be prevalent in education circles. And, we aim to provide some guidance on dispelling these myths in your classroom.  

First, there is no evidence to support learning styles (e.g., visual, auditory learners). While learners differ in their ability to comprehend different modalities, teaching students in their preferred modality does not lead to better performance (Howard-Jones, 2014). The Smithsonian has created a video titled, “Sending learning styles out of style” that aims to debunk this neuro-myth. Rather, evidence supporting the Universal Design for Learning suggests that learning is best supported through multiple means of representation (Meyer, Rose, & Gordon, 2012, 2014). 

Second, learners cannot multitask without performance cost. You can listen to the late Clifford Nas, Ph.D.’s interview on NPR titled, “The Myth of Multitasking” where he focuses on the cost of multitasking for creativity and concentration. Multitasking refers to the act of performing several tasks within a limited window of time, inclusive of both task-switching and dual-tasking (Koch, 2018). Often when we think we are multitasking, we are instead switching attention rapidly from task to task (e.g., task-shifting). In contrast, dual-task situations are those in which tasks are performed more or less simultaneously due to the temporal overlap of cognitive processes. Evidence suggests that studying, doing homework, learning during lectures, grades, and GPAs are all negatively affected by multitasking (Carrier et al., 2015). Learners need to be proactive participants. Learning is something that they choose to make happen, not something that “happens to them” as a reactionary response to teaching. 

Third, students underestimate how long a task will take to complete. This phenomenon of optimistic time prediction is referred to as the “planning fallacy,” which is when individuals overestimate how much they can accomplish in a given time period (Kahneman & Tversky, 1979). The planning fallacy occurs due to students focusing on how the future will unfold without integrating evidence from past experiences (Buehler et al., 1994). When students do consider previous instances of completing similar tasks, they consider only those that support an optimistic timeline. Even when confronted with past prediction failures students interpret those instances in diminished relevance to the present prediction. Conversations that implicitly involve the planning fallacy are unsurprisingly frustrating for both students and their instructors (e.g., Sanchez-Carracedo et al., 2018). Thus, it becomes pertinent to consider: How can instructors help students combat the planning fallacy? 

One answer lies in helping students perceive an assignment as requiring greater effort (e.g., Kruger & Evans, 2004). When a task is perceived as requiring greater effort students estimate longer time frames for completion. To increase perceived effort, instructors can help students move an assignment from the abstract to the concrete. This can be achieved by unpacking the whole task into subtasks. For example, the instructor could model visualizing each of the steps needed to complete a task. The other answer lies in helping students perceive a shorter timeline (Peetz et al., 2010). When a task must be completed in a short time period students demonstrate less optimistic time predictions (e.g., Eyal et al., 2004) and are more likely to accurately perceive potential obstacles to task completion (Peetz et al., 2010). To increase the perception of a shorter timeline, instructors should provide guidelines as to how much time students are expected to spend on different types of assignments and to have students consider realistic obstacles that could impact their task completion. 

Misconceptions: The link between the novice and expert

Studies have shown that experts and novices differ in how they experience everything from art (Silvia, 2013) to mathematics (Popescu et al., 2019). While few would argue against the value of expertise in a content area for teaching, expertise can also lead to misalignment of pedagogy. This disconnect is referred to as “The Expert Blind Spot” (Nathan & Koedinger, 2000). For example, those with more expertise are worse predictors of novice task performance times (Hinds, 1999). This may be due to expertise leading to automatization of cognitive processes (e.g., Ericsson & Simon, 1984). Moreover, those with no formal educational classes but strong subject matter knowledge demonstrate a mismatch in gauging difficulty (e.g., Nathan et al., 2001). Yet, the phenomenon referred to as the “Dunning-Kruger effect” demonstrates that novices also overestimate their abilities (Kruger & Dunning, 1999). Several studies have documented that students overestimate their own skill level because they do not know what they do not know (e.g., Howard et al., 2018). Thus, it is perhaps unsurprising that misconceptions litter the trail from novice to expert. 

Beyond a mismatch in gauging difficulty, there is often a discrepancy between our teaching beliefs, teaching intentions, student’s expectations and their reactions to teaching practices. For example, if an instructor believes that higher-order thinking skills are essential to a course but relies mainly on lecture-based teaching strategies then a discrepancy exists between learning goals and teaching practices. This gap between pedagogical teaching beliefs and teaching practices is found even in those celebrated for their teaching excellence (Owens, 2015). Similarly, alongside the heavily documented biases (e.g., race, ethnicity, sex) in university students’ evaluations of instructors (Reid, 2010; Wachtel, 1998), there is a well-documented gap between classroom pedagogy and students’ evaluation of teaching practices. Indeed, studies have shown that instructors who assign a lot of reading receive poor course evaluation (e.g., Howard et al., 2018). In fact, several studies have shown a negative relation between students’ evaluations of teaching and effectiveness of teaching: measured by long-lasting learning (see Kornell & Hausman, 2016 for review). Thus, high course evaluations do not necessarily indicate that instructors have taught students “how to learn” or that students have learned domain-specific knowledge.

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