Jitka Steinmetz - Going beyond engaging women in Computer Sciences in higher education

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Tackling Gender Disparities in Computer Science Education

Hello, everyone! We're very excited today to have here our amazing, strategic and commercially-minded leader, Jitka Taman. Jitka will share her key insights on going beyond engaging women in computer sciences in higher education.

A Bit About Jitka Taman

Currently, Jitka is the Managing Director, Germany at DXC. Lax Soft. She's a reliable partner for international corporations undergoing change, bringing a wealth of knowledge and strategy in her field.

The Current Status in Computer Science Education for Women

According to Jitka, the gender gap in computer science has been closing for the last 20 years. Despite this progress, the proportion of females to males among graduates has diminished from 30% in 2000 to 19% in 2019.

The dropout rate of female students in these fields remains twice as high, which is an alarming trend. Jitka believes the key factor that's been overlooked is the gender preferences in knowledge acquisition and learning.

When assessing her own education, Jitka reveals that the methods employed were sometimes at odds with female learning preferences.

The Past Approach and Current Diversification

The approach to closing the gender gap in computing has been, traditionally:

  • Getting girls interested in computing early on
  • Breaking gender stereotypes when raising girls
  • Female mentoring or role modeling
  • Empowering girls to pursue careers in computing

However, per Jitka, recent changes are focusing on taking gender into account in elementary education.

Research findings suggest that boys react better to symbols, whereas girls prefer inductive learning and discover rules from observing examples.

Such schools adapting diverse teaching tactics noticed a drastic reduction in discipline problems and increased number of students excelling.

The Continued Challenge and Potential Solutions

Even with such progress, women's dropout rates remain high. Female staff among the Faculty of Computer Science programs in Germany, for example, is rare, and the initial semesters that provide fundamental courses in computing still contain resources mostly written by men.

On the other hand, addressing potential gender-based learning preferences, such as inductive learning, concrete reasoning, visual sensory preference and context embedding has proven to significantly improve performance.

Jitka's Practical Example: The Turing Machine

Jitka shares her personal journey when studying computer science back in 2001 at the Technical University in Munich. Arriving in a class with a disproportionate number of males, she felt overwhelmed.

One of the subjects taught was the Turing Machine, explained using abstract diagrams and symbols. The boys seemed to grasp technical lecture while she found herself lost.

Jitka ultimately recognises that a different teaching approach could've helped her understand Turing machines as quickly as her male counterparts. A preferable method might have been a practical demonstration of Turing machines, a tangible image or a clearer purpose to understand the topic better.

Conclusion

Through Jitka's expertise, we understand the importance of recognising and addressing gender-based learning preferences in STEM studies to help reduce the dropout rates.

The challenge lies in carrying the same gender-diverse teaching methods identified in primary education, to higher education. This seems imperative to tap into the full potential of female talent in the tech industry.

As Jitka aptly concludes, a clear understanding, purpose and practical approach could indeed help overcome traditional gender disparities in computer science and higher education. Let's strive together for a more inclusive, diversified learning environment that serves everyone equally.


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