A Dual Heuristics and Bias Deeper Learning Model for Skill Development in Vocational and Technical Education

Author: 
Lim See Yew, Ramanath SH, Silas Wong Mun Yuen, and Yeo Hock Jin
November
2019
Volume: 
22
Number: 
11
Learning Abstracts

Today’s world is volatile, uncertain, complex, and ambiguous. Given these dynamics, it is imperative that teaching institutions innovate curriculum, assessment, and delivery strategies to equip students with good judgement in the face of uncertainty. In Asia, school lessons typically follow a factory model by which students experience hard work, rote memorization, test question preparation, private tuition classes, and summative assessments. As a result, individuals bring heuristics and bias to every activity (Tversky & Kahneman, 1974; Kahneman, 2011).

Model Design

The authors designed an experiment to explore a learning model in which a learner is able to connect explicit knowledge to a dual heuristics knowledge source to enable deeper learning and skill development. Both teacher and student bring tacit knowledge and bias to the learning environment; knowledge is deepened through embedded knowledge of the content. The learning model attempts to enable the learner to obtain mastery through corrective feedback given from the teacher’s heuristics and bias as well as the student’s own heuristics and bias; hence, the model involves dual heuristics and bias with engagement to the embedded knowledge in the learning process.

Dual Heuristics and Bias Deeper Learning Model

In a conventional knowledge transfer model, or learning model, all learning begins with the very core of content learning referred to as instructional, or explicit, knowledge. This category of knowledge is typically formalized and codified (Brown & Duguid, 1988) for easy memorization and understanding (Wellman, 2009). Mostly, this knowledge is transferred to learners in a classroom setting and assessed with summative written or oral tests.

In vocational and career preparation programs, teachers guide or coach learners in hands-on sessions in which heuristics tacit knowledge is acquired by students via faculty instruction (Polanyi, 1966). This knowledge is largely experience-based and is an outcome of explicit knowledge integrated with the heuristics and bias of the teacher. With this coaching, learners typically adopt the instructor’s heuristics and bias. Depending on the intrinsic bias of the learners, this knowledge could be used well by some students, but for others use may prove to be quite difficult, or even impossible.

Individuals typically frame outcomes differently due to different heuristics and bias. An individual can accept a certain level of external influences (Bandura, 1999). Regardless of the level of this acceptance, the learner’s capability for deeper learning is reduced as the learner basically just adopts the instructor’s heuristics and bias approach to resolve a problem rather than creating his or her own critical thinking approach. Assessments are typically procedural, formative, scenario-based (written), or test-based (oral). and, more or less, amount to recitation of information provided by the instructor. Learners will eventually grow in explicit and tactile knowledge over time through trial and error on their own after leaving the learning institution, but this model of learning is not effective in higher education settings.

The dual heuristics and bias deeper learning model taps into the embedded knowledge of the content as a primer to mesh the teacher heuristic and bias with the learners’ latent heuristic and bias coupled with explicit knowledge to create a new set of heuristics and bias specific to the learner and to the deep learning of skills (Horvath, 2000; Gamble & Blackwell, 2001). This approach hones the learners’ deeper learning and deep skills (see Figure 1).

In the proposed model, the objective is to create deeper student learning and skill development during a certificate program.

Figure 1: The Dual heuristics and Bias Deeper Learning Model

Experiment

Method

The experiment was carried out in a multiple-task work scenario. The complexity of the activities tested the robustness of the learning model. The work scenario is a computer numerical control (CNC) machining curricula to establish the best complete method for producing the work piece (see Appendix A and B). The procedures included the following:

  1. Analyse the overall task and development of an objective perspective;
  2. Plan the steps;
  3. Carry out the work steps and work organization with programming, manufacturing, and documentation;
  4. Present and discuss results; and
  5. Reflect on the results and the procedures (see Figure 2).
Figure 2: Experiment Procedures

Design

Students act as CNC machining specialists to create a cutting strategy using CAD/CAM to setup and manufacture the component on a 3-axis CNC milling machine. (See Figure 3.) The design steps in this process are listed below.

Figure 3: Cutting Strategy

Design Steps

Analyse the component drawings

  1. Study the drawing
  2. Study the component profile and tolerances
  3. Set the goal for machining

Plan the solution

  1. Study the material
  2. Select the appropriate cutting tools
  3. Plan the most appropriate cutting strategy for the component

Visualise and draw up the solution; provide the following information

  1. What are the range of options?
  2. Which tools are required?
  3. How should the CNC be programmed?
  4. Which CNC commands are needed for programming?

Create the work plan

  1. Choose the tool and select the technology
  2. Describe the working operations of the machine
  3. Create the CNC and CAM program
  4. Prepare manufacturing and documentation on a tooling sheet
  5. Machine the component

Data collection (embedded knowledge)

  1. Vibration
  2. Noise
  3. Spindle power
  4. Component quality measurement

Reflection

  1. Review of component quality
  2. Review of machining strategy
  3. Review of machining procedures
  4. Areas of improvement
  5. Assessment of methodical processes from post machining checks in accordance to benchmark data set

Results

Two groups of students were selected from the Higher National ITE Certificate in Precision Engineering program to observe and study the instructor’s work on the CNC machine and to produce a work piece (see Image 1 and Figure 4). The instructor, then, explains to students his approach and key points to ensure a good quality work piece (see Image 2).

Image 1 and Figure 4: Instructor Work Piece

Image 2: An Instructor Shares His Approach to Work Piece Production

Next, students are divided into two groups to produce the work piece. One group operates on a conventional CNC while another group operates on an Enhanced CNC that has incorporated sensors and a data analytics tool (see Image 3 and Appendix C) to record the embedded knowledge of the cutting process. The results of the group using the Enhanced CNC and the dual heuristics and bias deeper learning model is in Images 4 and 5 and Figures 5 and 6.

Image 3: A Student Using an Enhanced CNC to Produce a Work Piece

Images 4 and 5 and Figures 5 and 6: Cutting Performance Based on Dual Heuristics and Bias Deeper Learning Model

The group of students who worked on the enhanced CNC were able to finish the work piece faster, with higher quality, and displayed a higher confidence level than the group that did not. The improved ability of the enhanced CNC group shows that the investigated model led to higher scores and improved results. More investigative work is currently in progress to increase the product complexity and to further validate the model.

Conclusion

Many institutions are strengthening student learning through authentic experiences, enriching learning with education technology, and utilizing formative assessment over weighted summative assessment. The focus on teaching and learning diversifies from pedagogy to student motivation in learning, all with the intent of creating an active learner. This exploration of the Dual Heuristics and Bias Deeper Learning Model explores knowledge transfer and creation with deeper learning. The form of knowledge creation in a student anchors on the intrinsic cognition of the student as leverage to increase the understanding process. The outcome of the experiment conducted shows promising depth and rate of learning of a competency with the students creating knowledge through the dual heuristics and bias of both the instructor and student. Feedback gathered from students indicates that they gained self-confidence and were more engaged in thinking to resolve the work. This heightened realization relates to a more positive mind-set in learning than the instructor just leading and facilitating learning. The results also show that students are able to draw out deep skill in the model. The learning experience gained can be further enhanced with self-directed learning. These students will be truly independent and critical thinking learners. The long-term goal of this model should be to develop life-long learners.

References

Bandura, A. (1999). A social cognitive theory of personality. In L. Pervin & O. John (Eds.), Handbook of personality (2nd ed., pp. 154-196). New York: Guilford Publications.

Brown, J. S., & Duguid, P. (1988). Organizing knowledge. California Management Review, 40(3), 90-111

Gamble, P. R., & Blackwell, J. (2001). Knowledge management: A state of the art guide. London: Kogan Page Publishers.

Horvath, A. O. (2000). The therapeutic relationship: From transference to alliance. Journal of Clinical Psychology, 56(2), 163-173

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus & Giroux.

Polanyi M. (1966). The tacit dimension. Chicago: University of Chicago Press.

Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases, Science, New Series, 185(4157), 1124-1131.

Wellman, J. (2009). Organizational learning: How companies and institutions manage and apply knowledge. Basingstoke, UK: Palgrave Macmillan.

Appendices

Appendix A: Experiment Test Piece

2D CAD Drawing

3D CAD Drawing

Appendix B: Machining Process Sequence Sheet

Appendix C-1: Experiment Setup

Appendix C-2: Experiment Sensors Setup

Lim See Yew is Senior Director, School of Engineering; Ramanath SH is Senior Course Manager, Technical Engineer Diploma; Silas Wong Mun Yuen is Section Head, Precision Engineering, Advanced Machining Technology; and Yeo Hock Jin is Lecturer, Higher Nitec, Precision Engineering for the School of Engineering at the Institute of Technical Education College Central in Singapore.

Opinions expressed in Learning Abstracts are those of the author(s) and do not necessarily reflect those of the League for Innovation in the Community College.