Videos

https://edpuzzle.com/media/5ca27ac21ca47041527e85c8

https://ed.ted.com/on/Xb2Iaehu

Educational videos can be used in many ways to help teach online classes and conventional in person classes.  These types of videos can help promote learning in many different ways, but here are several. 

The first way they can benefit students is Mayer would say “promote active cognitive processes in students”.  This means that if the videos are tailored to the students correctly that they may have higher brain function that promotes learning.  This may even happen while students appear to be not learning.  If the videos are not tailored to the students correctly this will not happen.  For example: when I was in high school physics we would watch videos of Julius Sumner Miller. Julius’s videos were about physics; mostly basic physics and showed how the different theories worked.  While for myself, I enjoyed these videos.  I watched them with great intent and took something away from probably every video.  But in 2005 and with Julius being dead for 17 years (longer than many of the in the class students had been alive for yet) it wasn’t correctly tailored to the students (I was an anomaly), so promoting active cognitive processes in students it probably didn’t accomplish.

Silverman talks about multiple learning styles; this means that there are multiple learning styles.  Those styles are visual-spatial, auditory-sequential and tactile-kinesthetic.  Using videos allow us to hit multiple learning styles at once like audio and visual learners.  This allows those learners to learn using the best system they can.  Kozma found that if we use videos with spoken language, text, still images, and moving images the highest learning gains can occur using this media. 

Videos can also motivate students to learn. Motivating anyone to learn can be one of the hardest jobs of an instructor, because not everyone has the motivation to want to get praise or good grades.  Videos can be used to motivate students in several ways.  The way I have seen it used and the outcome can be very iffy. Is for an instructor to say “We learn this _____.  We can watch _______ video.”.  Whether or not the video has something to do with learning.  Students will normally push through whatever they are being taught, so they can get to that reward (the video).  Because of this push little to no learning can take place.  Videos can motivate learning by stimulating the learner.  This normally happens by taking something they like (a cartoon character) and putting it in a situation where it teaches something.  Sesame Street is a great example of this.  We take characters kids love and use them to teach letters, numbers, and life lessons. 

Visual media also stimulates a different portion of the brain, then let say just a book.  This can influence memory and can also influence cognitive learning.  Lets go back to my formative years.  In junior high, we watched the mini series Roots.  Doing this brought out emotional responses in the students on how slaves were treated.  These emotional responses have the ability to relay experiences and influence cognitive learning (Noble, 1983).

Lastly videos can take us places we normally wouldn’t be able to see/ learn from.  If we are talking about the Romans, students can view videos on the colosseum.  This shows them not only the place itself, but how things were built, how people lived, and the geography.  These can help push a lesson home or be a great Segway into a new lesson.  They help get the student’s attention.  This specific type could be replace with VR.

All in all videos are a great tool for the classroom. But we need to remember to use them correctly and not over use them.  Because too much of a good thing does hurt.

Computational Thinking

https://studio.code.org/c/827602367

Computational Thinking (CT) is a problem solving process that includes a number of characteristics and dispositions (Computational Thinking for Educators, 2019).  In other words, we are using computer sciences to support problem solving across different training scenarios.  There are many skills that computational thinking can improve, but we will only be talking about five: decomposition, pattern recognition, exploring algorithms, algorithm design, and critical thinking.

Decomposition is the breaking of problems down into smaller parts or more manageable pieces.  This allows students to focus on pieces of a problem rather than the whole.  Doing this makes problems seem less daunting and more doable for students.  Breaking problems or data into manageable pieces can help students with stress and it also shows them how the problem is put together.  Knowing how data is arranged gives the students a more in-depth look at the problem at hand.  In the Caterpillar program at Owens, we could use this to help strengthen troubleshooting skills and knowledge of how machines works.  If we break the machine down into parts, it is much less daunting for the student.  Students can take a machine component, like an engine, and, break it down into components parts (mixed up). Students can then take each component and put them in the order in which they are used.  

Pattern recognition is something that we do daily whether we realize it or not.  Actually humans will often find patterns where none exist because finding patterns gives people a sense of calm or order to their lives.  So it isn’t farfetched when using CT we may use pattern recognition.  Pattern recognition is finding and using a pattern to teach problem solving or other aspects of data.  So we may take students who are learning their shapes and give them a program.  This program will give them the first basic steps in drawing a shape and the student has to use pattern recognition to finish the shape.  After the student recognizes the pattern, we can add to it for them to make different shapes.  This can help them learn their basic shapes or go even further into geometry.

Exploring algorithms comes down to opening up the classroom to using algorithms and CT, but not actually writing code.  This means one may come up with “step-by-step instructions that can be used to solve a problem or carry out a task” (Computational Thinking for Educators, 2019).  We can use this in classrooms many ways.  We can have a machine problem and students have to gather information and put it in order to troubleshoot that problem, or students can use a map to figure out how to get from one place to another.

Algorithm design is when students use activities to extend pre-existing code.  If we go back to troubleshooting a problem on a machine, there are things called troubleshooting trees.  These give the student or technician a map on how to solve a problem.  We can give students part of the tree and they have to write the rest.  This would be considered a very basic algorithm design.

                The last skill that CT promotes that we will talk about is critical thinking.  Critical thinking isn’t mentioned outright in any of the text that talks about CT, but critical thinking is a big part of the results of teaching CT.  Critical thinking is the ability to analyze problems and solve them.  When we look at any of the activities from code.org, we have to analyze what needs to be done to successfully solve that problem or write that bit of code that completes the game the student is designing.  This skill is part of every skill we have gone through so far.

                One of the biggest rationales for introducing CT into the classroom is that it get students interested in coding or at least opens it up to them.  This helps the student decide if they might want to take that route for a career one day, helping fill the hundreds of thousands of jobs available to those who can code.  The secondary offshoot of this is that students are able to use technology in other courses that are not technology heavy.  This helps students retain the knowledge they are being given by making it fun and something they want to do, while helping them learn a new skill.

                When using Code Studio and Trinket had to use some math skills and a lot of critical thinking/ problem solving.  The directions most of the time were pretty vague and had to be interpreted.  This made for a lot of trial and error. Even when reading the code pages one had to do some guessing if they aren't familiar with these activities.  Getting the shapes to fill on the tree was a big one for me.  As well as doing shades of color for the dots.  Code studio I found to be a bit easier, but still had to do math and some trial and error to come up with the solutions.  I can see why these are so useful in the K-12 format. 

Computational Thinking for Educators. Retrieved from https://computationalthinkingcourse.withgoogle.com/course (2019).