Coding for Students with Disabilities

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來自 University of Illinois at Urbana-Champaign 的課程

Ubiquitous Learning and Instructional Technologies

個評分

University of Illinois at Urbana-Champaign

Ubiquitous Learning and Instructional Technologies

個評分

This course will analyze currently available technologies for learning. Areas addressed include: learning management systems, intelligent tutors, computer adaptive testing, gamification, simulations, learning in and through social media and peer interaction, universal design for learning, differentiated instruction systems, big data and learning analytics, attention monitoring, and affect-aware systems. Participants will explore the processes for selection and implementation of suitable technologies, the design of electronic learning resources, design and application of digital media in teaching and learning, familiarization with web usability and accessibility, and critical analysis of the benefits of technologies in education.

從本節課中

Goals and Outcomes

The final module explores some additional cases and issues of technology in learning, examining the nature of the classroom, computer coding as a curriculum practice, and finally the question of measuring learning outcomes.

What Is It and Why Do We Need It?6:36

Coding for Students with Disabilities2:46

Computer Science Teaching Practices11:33

Encouraging Student Collaboration3:49

A Case Study of Students with Disabilities6:07

與講師見面

Dr William Cope
Professor
Department of Education Policy, Organization & Leadership, College of Education
Dr Mary Kalantzis
Dean
College of Education

What about students with disabilities?

We're going to talk about the challenges that they face,

we're going to talk about biases that people

have about people with disabilities in the computing fields,

but then, most importantly,

we're going to talk about strategies for students with disabilities.

One of the important things to know is that students with

disabilities are heterogeneous groups,

so they're not going to all have the same kinds of struggles.

They are not going to have the same strengths,

they are not going to have the same motivations.

But, generally, we want to think about the technology and

the curricula and are those things inaccessible.

For example, students with visual impairments are going to have

a very difficult time with block-based programming languages that are very visual.

Students with learning disabilities that affect print,

for example reading disabilities,

are going to have a really difficult time with text-based programming languages,

which so much of the same struggles that they would have as they're

reading a textbook because they're having difficulties with print,

they're going to have with

text-based programming language as they read it and as they write it.

And then, students who have difficulties with memory are going to have

a really hard time with complex multi-step problems.

Students, for example, who are going to have communication disorders,

they're going to have a hard time with collaboration in computing environments.

So it's a very wide range of disabilities that are going to

affect how they are able to participate in computing.

The other important thing to remember is that computer science is supposed to be fun.

It's supposed to be exploratory and creative but that

very open-ended exploration can be very

difficult for students with disabilities if they don't have the background knowledge,

if they don't have the skills,

and if they don't have the persistent problem solving abilities.

So many students are going to quit as soon as they

reach a difficult task and they're not going to persevere.

So teachers are also going to have biases about who can do computer science.

One of the things is that computer science education is so new

so that when teachers see students who are

struggling they're going to think that they automatically just can't

rather than thinking about the kind of strategies that they can use to support them.

And the same kind of biases students are going to have,

they're going to think I'm really struggling with this, I can't do it.

And rather than thinking there are things I can do to learn how to do this.

They're going to think well, I just can't because I'm not smart enough.

And so those biases are really problematic.

So when we talk about computer science for all what do we really mean by that?

And so what I mean by that is that we need to find that pedagogical approaches

to support a much broader range of learners so that we start to dispel these biases.

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