The document discusses various teaching methods and skills for computer science education, including problem-based learning, active learning, assessment of skills, and delivery of skills. It provides examples of problem cases and learning goals for problem-based learning in theoretical computer science. Tables classify problems and settings as open or closed. Peer instruction, robots games, downsides, and next steps are also mentioned. References on teaching approaches, activities, and reflections on programming education are listed.

1. Oh
HELLO
there
(intro slide: what? how? why?)

2. What do students
GAIN
from our subject?

4. How do we
ASSESS
these skills?

5. How do we
DELIVER
these skills?

6. How do we
?
these skills?

7. How do we
NURTURE
INSTILL
ENCOURAGE
PROMOTE
ELICIT
INSTIGATE
INSTANTIATE
these skills?

8. TEACHING

9. OBJECTIVISM

10. ACTIVE
learning

11. Trigger
ACTIVE
Activity
learning
Discussion
Summary

12. ACTIVE
learning
for CS

13. P RO BL EM CA SE
Problem
TABLE I
S AN D TH EI R
LE AR NI NG GO
AL S .
Topic
Case 0
Case 1
Problem
based
learning
Morse alphabet
– Basic concepts
Problem solving
company – Mod
elling problems,
their difficulty an
d solvability
Case 2
Search for mail
addresses and a
precompiler for
a programming lan
guage – Regular
Case 3
expressions
Coffee automato
n – Deterministic
finite automata
Case 4
Nondeterministic
editor – Nondete
rministic finite automata an
d determinization
Case 5
Roman checking
exams – Finite au
tomata vs.
regular expressio
ns, ≤-automata
Case 6
Grandma’s rhyme
– Pumping Lemm
Case 7
a
L-systems – Idea
of grammars
Case 8
Parentheses parsi
ng – Pushdown
automaton
Case 9
Arithmetic calcu
lator – LL(1)-gram
mars,
recursive parser
Case 10
General parser –
The CYK-algorith
m and
Chomsky normal
form
Extra
Attribute gramma
r and parser tools
Case 11
Summing machine
– Basics of Turin
Case 12
g machines
Library functions
for TM’s – Deep
er practice
on Turing machine
s
Case 13
Programming co
mpetition – Unive
rsal machines
and universal lan
guages, solvability
Case 14
proofs
Philosophical co
nsiderations – Ch
urch-Turing
theses, limits of
computation.
Case 1
Problem solving
company again –
Overview
revisited of the
principles learnt
C LA SS IFI CATI ON
Problem 0
Problem 1
Problem 2
Problem 3
Problem 4
Problem 5
Problem 6
N UM BE RS OF
TR AD IT IO NA
L (T
TH E PROB LE M
- BA
TABLE II
OF TH E PROB
LE M SE TT IN G
, GOAL S AN D ME
TH OD S AS
OP EN o OR CL
OS ED c.
Problem
the course
to the tradi
from the c
based learn
approach w
50%. 55 stu
and seven s
students, and
The exact n
It is rem
dropped from
had seen wh
dropouts of t
staff’s resour
problem-base
of it. In the tra
usually much
we can conclu
to a problem-b
Setting
c
o
o/c
c
o/c
o/c
o
Goal
c
o
c
c
o/c
c
o/c
Methods
o
o
o
c
o
o
Problem-based learning of theoretical computer science (Hamalainen 2004)
m
d
f
m
fa
fe
m
pa
fem

14. Success in Introductory Programming: What Works?
(Guzdial, McDowell, and Simon 2013)
Peer instruction

15. The
Robots
Game
The Robots Game (Wray 2013)

16. What are the
DOWNSIDES?

17. What’s
NEXT?
Guide to Teaching Computer Science: An Activity-Based Approach
(Hazzan, Lapidot, and Ragonis 2011)
Reflections on the Teaching of Programming: Methods and Implementations
(ed. Bennedsen, Caspersen, and Kölling 2008)