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  • monash.edu

    Designing authentic assessment

    Tina Overton School of Chemistry

  • The challenge

    The employable

    graduate

    Subject knowledge

    Problem solving, communication, team work, time

    management, handling data, etc

    Professionalism, creativity,

    entrepreneurial, global

  • The challenge

    The employable

    graduate

    Subject knowledge

    Problem solving, communication, team work, time

    management, handling data, etc

    Professionalism, creativity,

    entrepreneurial, global

  • What is authentic assessment?

    • Authentic genuine real

    • Using and applying knowledge and skills in a real world setting

    • in which students are asked to perform real-world tasks that demonstrate meaningful application of essential knowledge and skills (Meuller, 2014)

    • Smilariry betgtween thinking required for assessment task and real life situation (savery & Duffy, 1995)

    • Use of same skills, knowledge, attitudes that would need to apply in professional life (Gulikers et al , 2004)

  • • Aa must be aligned to a instruction (Biggs, 1994)

    • Students demonstrate competencies through signigcant and meaningful r=tasks (Wiggins, 1993)

    • Authenticity is subjective. Must be oerceived as authentic ton students to influence learning

    • Task mimics professional role

  • Constructive alignment

    Learning outcomes

    Assessment L&T

    activities

  • Science TLOs

  • Assessment of learning

    Assessment for learning

    Knowledge Application,

    competences, skills

    Lower order cognitive skills

    Higher order cognitive skills

  • 21st Century science graduates.....

    • Are entering a rapidly changing workplace

    • Will tackle as yet unthought-of of problems

    • Will tackle global challenges

    • Live in an information-rich and connected society

    • Will have to be

    – Flexible, entrepreneurial, creative, problem solvers, global citizens.

    • Are paying for their HE

  • How do we prepare them for this?

    Learning outcomes

    Assessment L&T

    activities

  • How do we prepare them for this?

    Learning outcomes

    Assessment

  • How do we prepare them for this?

    Learning outcomes

  • How do we prepare them for this?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • What does 21st Century pedagogy look like?

  • Have things changed?

  • Does it work?

  • The drive to innovate

    • make changes in something established, especially by introducing new methods, ideas, or products

  • What about impact?

    • ‘Evaluation of teaching’

    • Evaluation

    – Determine value or worth

    • Did it work? How do we evaluate?

    – For who?

    – Happy sheets?

    – Questionable data

    – Beware the Hawthorn effect

  • From evaluation to research

    • Quality and quantity of data

    • Identify meaningful learning gains

    • Understand how students learn

    • Attitudes, aspirations, experiences

    • Collect valid, reliable, transferable evidence

    • Evidence informed innovation and change

    • Build the discipline

  • Why bother?

    • Improve student outcomes and experience

    • Convince others

    • Reinventing the wheel

    • Build your CV

    • Publications

    • Funding

    • Reward

  • Some personal favourites!

  • Information processing model Miller, G.A., (1956), The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97.

    Events

    Instruction

    Attention

    Interpreting

    Rearranging

    Comparing

    Processing

    Response

    Storage

    Linked

    Discreet

    Working Space Long Term Memory

    Perception Filter

    Storing

    Retrieving

  • Cognitive load in learning science Johnstone, A.H., El-Banna, H., (1986), Capacities, demands and processes: a

    predictive model for science education. Education in Chemistry, 1986. 23(3),

    80-84.

  • Cognitive load in the lab Johnstone A.H., Watt A. and Zaman T.U., (1998), The students’ attitude and cognition change to a physics laboratory, Physics Education, 33, 22-29.

    Johnstone A.H., Sleet R.J. and Vianna J.F., (1994), An information processing model of learning: its application to an undergraduate laboratory course in chemistry, Studies in Higher Education, 19, 77-88.

    Better performance, retention, attitudes

    Pre-lab

    Skills training

    Clear aims

  • Cognitive load in the lecture Sirhan G., Gray C., Johnstone A.H. and Reid N., (1999), Preparing the Mind of the Learner, University Chemistry Education, 3(2), 43-46.

    Remove lecture time

    Replace with background

    reading

    Improved exam results

    Loss of correlation

    with previous background

    Pre-lectures

  • Active learning Bonwell, C.C., and J. A. Eison, Active Learning: Creating Excitement in the Classroom, ASHEERIC Higher Education Report No. 1, George Washington University, Washington, DC , 1991.

    They must read, write, discuss, or be engaged in solving problems…..to be actively involved, students mast engage in such higher-order thinking tasks as analysis, synthesis, and evaluation….strategies promoting active learning be defined as instructional activities involving students in doing things and thinking about what they are doing.

    …leads to better student attitudes and improvements in students’ thinking and writing……surpasses traditional lectures for retention of material, motivating

    students for further study and developing thinking skills

  • xBL • Context-based learning

    – Enhanced motivation and attitudes

    Pilot A. & Bulte A., (2006), Why Do You “Need to Know”? Context‐based education, International Journal of Science Education, 28, 953-956.

    • E/Inquiry-based learning

    – Deep learning, research skills, nature of science, transferable skills

    Brickman P. et al, (2009), Effects of Inquiry-based Learning on Students’ Science Literacy Skills and Confidence, International Journal for the Scholarship of Teaching and Learning, 3, 1-24.

    • Problem-based learning

    – Deep learning, motivation, research skills, interdisciplinarity, range of skills

    Boud D & Feletti G, (1991), The Challenge of Problem-based Learning, Kogan Page.

  • Always detractors

  • Flipping or flopping?

  • The pen is mightier….

    Meuller P. & Oppenehimer D., (2014), The pen is mightier than the keyboard: Advantage of longhand over laptop notetaking, Psychological Science, 25(6), 1159- 1168.

  • So why?

  • How do we convince? • Present meaningful evidence

    • Lead by example

    • Mentoring, support, training

    • Link to employability

    • Use students as advocates

    • Student representation

    • Peer observation/assessment

    • Define professional standards

    • Retention

    • Preparation for honours and PhD

    • Criteria for reward, recognition, promotion

  • How do we get there?

  • And when it seems hopeless….

  • Thank you for listening

    [email protected]

    @tinaoverton