Education systems may be primarily concerned in most cases with the content of their lessons, and not very much with their instructional delivery. However, the effective application of learning theories in education especially in higher educations has an impact on student performance. In following a summary of studies about the applicability of three learning theories as constructivism, behaviorism and cognitive load theory in computer science education are provided. However, student success in learning computer science is not limited to only those three learning theories. In general, research findings suggest that effective computer science instruction involves the design of cognitive models that facilitate retrieval, and a consistent application of positive reinforcement.

Constructivism is a theory of learning, which claims that students construct knowledge rather than merely receive and store knowledge transmitted by the teacher. Constructivism has been extremely influential in science and mathematics education, but much less so in computer science education (CSE).

Constructivism claims that each individual will perform the construction differently, depending on his or her preexisting knowledge, learning style and personality traits. The task of the teacher in the constructivist paradigm is significantly more difficult than in the classical one, because guidance must be based on the understanding of each student’s currently existing cognitive structures.

Constructivism does have a lot in common with discovery or inquiry learning, where students are expected to discover knowledge by themselves. Given the rapid rate of change of software tools and applications, most software engineers in industry and business are continually engaged in education: not only in formal training sessions, but also—perhaps more importantly—in the development and application of manuals, interfaces, and help files. So this theory is extremely relevant to their day-to-day work.

 Skinner (1958) has found that “behavior is shown to be shaped and maintained by its ‘reinforcing’ consequences. Behaviorists focus more on how students respond to positive and negative reinforcement provided through an educator’s planned system of data presentation.

Behaviorism has seen the Teaching Machine Phase, the Programmed Instruction Phase, and the Systems Approach to Instruction. The Teaching Machine is perhaps of the most interest when examining educational technologies of today, as the machines were very basic versions of what educational software and computers can accomplish now.  The teaching machine was, in essence, a box that sat on student desks that each individual student could use to record answers to certain prompted questions.

An example of the classroom use of the Teaching Machine is as follows.  “In using the device the student refers to a numbered item in a multiple-choice test.  He presses the button corresponding to his first choice of answer.  If he is right, the device moves on to the next item; if he is wrong, the error is tallied, and he must continue to make choices until he is right”. We can clearly see the similarities between the Teaching Machine and much of today’s instructional computer software, designed for reinforcing student behavior. Computers and software are, in essence, much more complex versions of the Teaching Machine. This type of learning, where a “student is rewarded through an encouraging comment before moving on to the next learning objective” is especially apparent in the use of “the computer games that are so highly addictive to teenagers,” as their “learning behavior is being progressively rewarded as each level of the game is mastered”.

Information processing theory discusses the mechanisms through which learning occurs. Information gathered from the senses (input), is stored and processed by the brain, and finally brings about a behavioral response (output).This theory specifically focuses on aspects of memory encoding and retrieval.

 Cognitive load refers to the total amount of mental effort being used in the working memory. Cognitive load theory was developed by John Sweller in the late 1980s. This theory employs aspects of information processing theory to describe how the mind acquires and stores knowledge and to emphasize the limitations of concurrent working memory load on learning. It describes two mechanisms to circumvent the limits of working memory:

1) Schema acquisition, which allows us to chunk information into meaningful units

2) Automation of procedural knowledge.

The process of chunking information into meaningful units is quite similar to the way a computer programmer would combine steps in a program into an abstraction. For example, the individual programming steps of finding the sum of a series of numbers, then dividing the sum by the number of numbers would be combined together (i.e. chunked). This abstraction has a convenient name as average.

Schema acquisition involves more than chunking. The information that is chunked is further processed and placed into a schema. Children learning to read, for example, have to build schemas for letters that allow them to classify an infinite variety of shapes into the limited number of characters in the alphabet.

The second mechanism that circumvents working memory, automation of procedural knowledge, deals with skills acquisition. Once a particular skill is acquired, automatic processing can bypass working memory. In other words, with enough practice, an activity can be carried out without conscious processing. For example, many individuals reach a stage in driving where their conscious, or working, memory is free for other activities.

Teaching in technical areas is often based on the formula of presenting a new topic, showing a few examples, and assigning practice exercises. Some simple adjustments to the presentation-examples-practice formula can accommodate cognitive load theory.

Here is one simple example that how teacher can facilitate initial and correct schema formation. Following presentation, students can work through several examples with the teacher or examine multiple problems with integrated solutions, then immediately practice problems of the same type. By repeating this process with each type of problem, students can build schemas, and reinforce learning by retrieving and refining those schemas.

One approach to reducing cognitive load is to increase working memory capacity by utilizing verbal and visual channels. One effective approach is to integrate text into an associated graphic. By placing the text directly within a diagram, the student is not forced to split attention between multiple sources. Modern software, in the form of algorithm animation and multimedia presentation packages where text and graphics can be added a piece at a time, gives much promise for this approach. In addition, algorithm animation and multimedia give students’ power over the speed in which the material is presented, and therefore redundant learning elements can be reduced.