Print Page | Contact Us | Sign In | Join
Award for Teaching Excellence Recipient: Daniel Moix
Share |

 


Daniel Moix

Arkansas School for Mathematics, Sciences, and Arts

Little Rock, AR

Twitter

Email

"Being able to eat an elephant one piece at a time is a valuable skill whether you’re debugging a sorting algorithm, or making college plans."

 

Having taught computer science (CS) in Arkansas since 2003, Daniel Moix believes that anyone can learn CS. Some learn it more quickly, with less effort, or in a language other than English. Some learn it for the sake of learning something new. Others learn it because they want to solve a problem, complete a task, or change the world. Thinking and problem solving abstractly is key, where one deconstructs complex processes and systems as well as constructs solutions from smaller parts---not only to solve problems and create software solutions, but it also translates into many other aspects of life.

 

Being able to “eat an elephant” one piece at a time is a valuable skill whether you’re debugging a sorting algorithm, or making college plans. Once students can identify and use abstractions, they transition into creating their own abstractions by defining functions, methods, and even new abstract data types. Throughout the process, students simultaneously develop their abilities to decompose problems into simpler parts and construct solutions with an increasing mastery of software development tools and techniques.

 

Daniel has spent almost 15 years of teaching at several schools including the Arkansas School for Mathematics, Sciences & Arts; College of the Ouachitas; and Bryant High School. For Daniel, it is very important to constantly seek ways to make CS relevant to each learner, regardless of the school. His decision-making process when selecting instructional methods and strategies when teaching CS depends on many factors: audience, previous experiences, anticipated outcomes, how long he has with the learners, and the communication channels available.

 

For face-to-face interactions at a residential high school for gifted students, Daniel can leverage students’ prior learning in programming, algorithms, and data structures. The size of the school and the small student-teacher ratio in these “senior-level” courses provide the flexibility to be directly engaged with each learner on a very personal level. A Special Topics in Computer Science course has only 8 students, each one working on a community service project of choice. The scale permits regular one-to-one interactions. Daniel regularly uses formative assessment tools such as questioning to gauge mastery. For gifted students, or those with exceptional prior knowledge, he encourages students to “go the extra mile,” by extending work into an unfamiliar avenue or finding ways to share knowledge with others.

 

For large projects, it is important for students to iterate through a series of self, peer, and final evaluations. Students must evaluate their own work using a rubric or scoring guide and write reflections on why. They then have an opportunity to make revisions to the work before it is scored by peers. In this iteration, students give and receive evaluation from others. A second or third set of eyes reveals strengths and weaknesses in the work that the student may have missed and provides insight into how others interpreted the assignment. Finally, students process the peer feedback and make final revisions before submission.

 

One strategy particularly effective to reach English Language Learner (ELL) students is to provide draft copies of assignments to them. ESL students highlight any words, phrases, or instructions that seem unclear. In turn, Daniel works with high-level bilingual students to revise the assignment drafts in clearer language, accessible to all students. This approach gives these students more time with the materials, a meaningful opportunity to closely read them, and an opportunity to shape the classroom experience for their peers.

 

At the other end of this spectrum are remote students, enrolled in school districts of all sizes across Arkansas. They have diverse backgrounds, varied interests, and incredibly mixed ability levels. He teaches and assesses remote learners through live video conferences, recorded lessons and demonstrations, and occasional face-to-face contact. He intentionally designs instruction to be readable by students with limited literacy skills, while providing avenues to go above and beyond the minimum requirements for average, above average, and truly gifted students with “extras” and “challenge” opportunities.

 

Daniel also works with adult learners. The types of decisions that go into designing these learning experiences (professional development for teachers) include finding out the answers to questions such as: “Who are you, why are you here, what do you know, and where do you want to be by the time you leave?” His teaching can be any of the following: one-hour breakout sessions on content, curriculum, and pedagogy at conferences and summits; day-long workshops for teachers and administrators at all levels of CS mastery; and 4-6 day “boot camp” experiences for groups of teachers who are new to CS.

 

In additional to his full-time teaching role in Arkansas, Daniel contributes in multiple ways by serving as a volunteer in state, national and international organizations committed to Computer Science. This includes serving as Computer Science Teachers Association (CSTA) Arkansas Vice-President, a member of the CSTA Computer Science Advocacy Leadership Team (CSALT), and Arkansas’s first K-12 Computer Science Education Specialist. Daniel was the 9-12 grade level lead for the 2016 CSTA K-12 Computer Science Standards and a writer on the recently-released Framework for K-12 Computer Science Education. Daniel is a recipient of the 2015 Presidential Award for Excellence in Mathematics and Science Teaching and the 2016 ACM/CSTA/Infosys Foundation Awards for Teaching Excellence in Computer Science.

 

Our Partners

© 2005-2017

The Association for Computing Machinery founded CSTA as part of its commitment to K-12 computer science education

Computer Science Teachers Association (CSTA)
230 Washington Avenue Extension, Suite 101
Albany, NY 12203-5390
800.694.8320