GP 7 Plan and assess for deep learning

In essence

Deep learning occurs when students use appropriate cognitive processes to engage in a meaningful way with the task. When students pay attention to the learning process and critically reflect they are engaged in deep learning. When learning activities and assessment tasks allow them to experience understanding at a deep level and not just reproduce content, learning can be personally transformative.

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What you can do

Take a student centred and learning oriented approach to your teaching. Use varied assessments that require demonstration of understanding and encourage students to think for themselves, eg essays, applications for new concepts and problem-based questions. Encourage students to develop deep approaches to learning.

When designing curriculum:

  • Provide overarching goals and clear aims
  • Identify generative, open topics
  • Emphasize understanding in learning outcomes
  • Incorporate authentic, relevant topics

When teaching:

  • Relate teaching directly to prior knowledge
  • Teach to clarify meanings and stimulate interest
  • Encourage meta-cognitive awareness and self-regulation in studying
  • Emphasize principles and concepts
  • Use techniques to promote understanding such as open ended questions

When assessing:

  • Use formative assessment designed to develop understanding
  • Use feedback to clarify and reinforce understanding
  • Use marking criteria that describe levels of understanding
  • Use assessment strategies that encourage and reward conceptual understanding (Entwistle, 2001).

What it looks like

Deep learners:

  • aim to understand ideas on their own
  • learn by actively transforming their perceptions and conceptions
  • relate ideas to previous knowledge and experience
  • look for patterns and principles
  • check evidence and relate it to conclusions
  • examine logic and argument cautiously and critically
  • are aware of the understanding that develops while learning
  • are actively interested in content
  • find the experience emotionally satisfying (Entwistle, 2001).

Myths busted

Multiple choice questions (MCQs) are typically associated with promoting surface learning but MCQs are shown to be associated with deep learning in research conducted by Draper (2009) This study found that the context in which MCQs are applied is the determining factor in the type of learning that occurs. Deep learning using MCQs can happen when students are channeled to focus on the relationships between items rather than engaging in rote recall of unassociated facts. This research presents six ways for using MCQs to promote effective and deep learning using electronic voting systems (EVS) and e-assessment.

Another study investigating the effectiveness of MCQs found that the format of assertion reason questions (ARQ) is superior to standard MCQ items and can support deep learning. However, the ARQ testing is recommended as better applied and more effective for learning as a formative assessment tool in a self-paced online environment (Williams, 2006a).

Final exams can promote deep learning if, as Williams (2006b) describes, they are based on authentic assessment principles where students engage with interesting and relevant real problems and are required to draw on relevant skills and knowledge to find solutions.

How it is applied in disciplines

Design: design history is taught to first year students using strategies that encourage deep learning is described. Conducting the course in this way involved designing content around themes to build history and theory, requiring students to develop personal historical timelines, using lectures, tutorials, texts and online resources to deliver learning material, and providing scaffolding and support for assessment tasks. Read more Blackler & Sim (2007).

Design: Lecturers in a graphic design course analyzed the influence their assessment strategies had in encouraging surface learning and as a result made changes so that students were more likely to adopt deep learning. These changes included enhancing their approaches to providing feedback, improving the use of formative assessment and introducing authentic assessment tasks. Read more Ellmers, Foley & Bennett (2008).

How it is applied in teaching contexts

Examinations: an open book open web (OBOW) context for final examinations is described. It uses authentic assessment principles in providing real problems to which students apply current knowledge and skills rather than reproduce content. Read more Williams (2006b).

Bridging tools:video is used as a bridging tool to demonstrate the research process in a marketing course. The strategy helped students to more confidently approach the deep learning strategies they were required to undertake. They were able to move more quickly to thinking critically and synthesizing and analyzing material. Read more Ryan, Ogilvie & Bevilacqua (2007).

What should you think about when implementing deep learning in your teaching

Answer Yes to these

Teaching philosophy: I believe in student-centred learning. I think about the contribution I am making to the development of students skills and capabilities for their future professional lives.

Pedagogies: Do active and self-directed learning approaches which characteristically emphasize constructivist learning suit my teaching?

Curriculum: What are the learning outcomes I wish my students to have in my course?

Tools for learning: How will I motivate my students to adopt active and self-directed learning strategies? How will I incorporate active and self-directed learning into my course?

Commitment: Can I identify an ‘active or self-directed learning’ champion who will support innovative practice?

Why is it important?

The aims of higher education are many but importantly include that students engage in deep learning that enables them to undergo conceptual change in their thinking and understanding of self as well as acquire disciplinary information and content (Biggs, 2003). Deep learning behaviors promote more meaningful learning in learners (Nelson Laird, Shoup, Kuh, & Schwarz, 2008).

Deep learning which focuses on the meaning of tasks, interconnected ideas and integrated structures can foster independent learning abilities (Walker, Brownlee, Lennox, Exley, Howells & Cocker, 2009). Learning in higher education is also about encouraging students to be self-directed so that they can determine their own learning needs, set learning goals, take action to meet necessary requirements and evaluate their work for both their immediate responsibilities as students and future circumstances as professionals (Biggs, 2003).

What is it and how does it support learning? What does recent research say?

A deep achieving approach is the most desired attribute for higher education learning. Deep achieving learning is most effective when students are aware of their own learning processes and adopt strategies that match their motives for learning (Biggs, 1987).

Students can approach learning in three ways. A deep approach to learning is typified by a personal commitment to understand material and is demonstrated by behaviors such as wide reading and relating new knowledge to what is already known. It leads to an understanding of the complexity of a task and students will have positive feelings about their learning. Surface learning is characterized by doing the minimum and engaging in rote learning and memorization. It does not tend to achieve a complex understanding of concepts and students’ associated feelings are often dislike, dissatisfaction or boredom. Achieving learners are well organized and motivated to achieve high marks and will combine this approach with either deep or surface strategies to align with their learning motives (Biggs, 1987; 2003).

Extensive research shows that effective learning environments promote deep learning approaches (Nelson Laird et al, 2008). While personal ability affects a student’s approach to learning, the actual learning task and learning conditions also have a large influence on learning approach (Biggs, 2003).

Students’ approaches to learning are also influenced by the beliefs they hold about learning (Walker et al, 2009). Understanding the range of students’ backgrounds and their underlying beliefs about learning can lead to using more effective ways of learning and teaching that guide them to adopt deep learning approaches and achieve course goals. In a study that investigated first year students it was found that learning beliefs were related to program of study, gender, age, educational experience and family experience at university (Walker et al, 2009).

Assessment needs to support learning rather than measure learning. Student learning is most influenced, not by teaching, but by the type of assessment systems and the way feedback is used (Gibbs & Simpson, 2004-5). Conditions that can influence students to learn more effectively include having enough time to learn, using coursework assignments rather than exams, providing formative assessment tasks and giving effective and timely feedback (Gibbs & Simpson, 2004-5).

Recent research found that students in soft, pure and life disciplines tend to adopt deeper learning strategies than those studying hard, applied and non-life disciplines (Nelson Laird et al, 2008). However, students in any discipline who engage in deep learning, experience high personal and intellectual development along with greater satisfaction levels with their studies (Nelson Laird et al, 2008).

References

Biggs, J. (1987). Learning Process Questionnaire manual: Student approaches to learning and studying. Hawthorn, Melbourne: Australian Council of Educational Research.

Biggs, J. (2003). Teaching for quality learning at university: What the student does. Maidenhead, Berkshire: Open University Press.

Blackler, A., & Sim, J. (2007). History for designers : engaging first years across disciplines. Paper presented at the Engage, regenerate, experiment 10th Pacific Rim [on] First Year in Higher Education Conference.

Draper, S. W. (2009). Catalytic Assessment: Understanding How MCQs and EVS Can Foster Deep Learning. British Journal of Educational Technology, 40(2), 285-293.

Ellmers, G., Foley, M., & Bennett, S. (2008). Graphic design education : a revised assessment approach to encourage deep learning. Journal of University Teaching and Learning Practice, 5(1), 77-87.

Entwistle, N. (2001). Promoting Deep Learning through Teaching and Assessment. In L. A. Suskie (Ed.), Assessment to promote deep learning (pp. 9-19). Washington, DC.: American Association for Higher Education.

Gibbs, G., & Simpson, C. (2004-05). Conditions under which assessment supports student learning. Learning and Teaching in Higher Education, 1, 3-31.

Nelson Laird, T. F., Shoup, R., Kuh, G. D., & Schwarz, M. J. (2008). The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes. Research in Higher Education, 49(6), 469-494.

Ryan, M. M., Ogilvie, M., & Bevilacqua, A. (2007). Using a bridging vehicle to increase deep learning : a videography teaching study. Paper presented at the ANZMAC 2007 : reputation, responsibility, relevance.

Walker, S., Brownlee, J., Lennox, S., Exley, B., Howells, K., & Cocker, F. (2009). Understanding First Year University Students: Personal Epistemology and Learning. Teaching Education, 20(3), 243-256.

Williams, J. B. (2006a). Assertion-reason multiple-choice testing as a tool for deep learning : a qualitative analysis. Assessment and Evaluation in Higher Education, 31(3), 287-301.

Williams, J. B. (2006b). The place of the closed book, invigilated final examination in a knowledge economy. Educational Media International, 43(2), 107-119.