1. Asking questions to find out about students’ knowledge about self-teaching

2. Students as self-teachers
Most of the time, students must teach themselves.
Most students do not get much individual teacher attention
Black (2004) in a UK primary school observed an average of 20 interactions per student over 24 lessons
= 0.8 interactions with a student per lesson
Black, L. (2004). Language and education, 18,

3. Eliciting students’ instructional metacognitive knowledge
So if students must teach themselves, what self-teaching knowledge, or, instructional metacognitive knowledge, do they have?
How do we find out?

4. We ask them questions about their knowledge

5. Our fundamental question
What do you do to help yourself to learn?
(followed by various prompts to get students to talk about the fundamental question)

6. How have we asked this question?
In class , whilst students are working at learning
Face-to-face (audio record)
Written responses (students hand up to researchers)

7. Has this fundamental question generated useful data that can be analysed?
Text-based data
Count-based data
Ranked data

8. Text extracts from individual students
I don’t know what really helps me to learn…I don’t know how that helps me to learn, it just does. It’s just something I’ve never questioned, it just helps me…it’s just the way I’ve learned to survive while I’m doing these things. But I don’t know how. (B.Ed student)

9. Summaries of answers from classes of students
Year 9 Science students’ responses

10. Text-based: change over time

11. Thematic analysis with counts (frequencies)
Pre
Post
Stage of processing
E.g %
Orientation
Finding a way to be interested 3
Selecting
Paying attention so it made sense 6
Transformation (simple)
Repeating over and over again 45 26
Transformation (complex)
Developing acronym for terms 9
Organising
Summarise, make diagram, concept map 12
Retrieving and using
Rewrote until I knew what it mean 40 29
Monitoring
Answering questions, reflecting 18
Total 33 34

12. Profiling components of students’ knowledge (frequencies)

13. Dimensional analysis (count-based)

14. Cluster analysis (ranked data)