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ORCIDRachel Or-Bach
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2010 – 2019
- 2013
[j8]Rachel Or-Bach, Bert Bredeweg:
Support options provided and required for modeling with DynaLearn - A case study. Educ. Inf. Technol. 18(4): 621-639 (2013)- [j7]Rachel Or-Bach, Marije van Amelsvoort:
Supporting Asynchronous Collaborative Learning: Students' Perspective. Int. J. Online Pedagog. Course Des. 3(4): 1-15 (2013) - 2012
- [c7]Rachel Or-Bach, Bert Bredeweg:
Collaborative Reflection Activities with DynaLearn. ICALT 2012: 134-137 - 2011
- [j6]Ilana Lavy, Rachel Or-Bach:
ICT literacy education: college students' retrospective perceptions. Inroads 2(2): 67-76 (2011) - 2010
- [j5]Aharon Yadin, Rachel Or-Bach:
The Importance of Emphasizing Individual Learning in the "Collaborative Learning Era". J. Inf. Syst. Educ. 21(2): 185-194 (2010)
2000 – 2009
- 2009
- [c6]Rachel Or-Bach:
IS education - An Empirical Study Of Individually Assigned Homework. MCIS 2009: 8 - 2008
- [j4]Aharon Yadin, Rachel Or-Bach:
Fostering individual learning: when and how. ACM SIGCSE Bull. 40(4): 83-86 (2008) - 2006
- [c5]Rachel Or-Bach:
The role of a KBS course within IS education. ITRE 2006: 152-155 - 2005
- [j3]Rachel Or-Bach:
Educational benefits of metadata creation by students. ACM SIGCSE Bull. 37(4): 93-97 (2005) - 2004
- [j2]Rachel Or-Bach, Wouter R. van Joolingen:
Designing Adaptive Interventions for Online Collaborative Modeling. Educ. Inf. Technol. 9(4): 355-375 (2004) - [j1]Rachel Or-Bach, Ilana Lavy:
Cognitive activities of abstraction in object orientation: an empirical study. ACM SIGCSE Bull. 36(2): 82-86 (2004) - 2003
- [c4]Rachel Or-Bach:
Design Considerations for Supporting Collaborative Modeling. ICALT 2003: 219-223 - [c3]Rachel Or-Bach, Ilana Lavy:
Students' understanding of object orientation. ITiCSE 2003: 251
1990 – 1999
- 1992
- [c2]Rachel Or-Bach, Ehud Bar-On:
Why Should an ITS Bother with Students' Explanations? Intelligent Tutoring Systems 1992: 372-381
1980 – 1989
- 1986
- [c1]Ehud Bar-On, Gideon Amit, Rachel Or-Bach:
Knowledge representation model which combines conceptual graphs and fuzziness for machine learning. IPMU 1986: 307-318
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