EDUCATIONAL NEED
In a short overview, the main reason that current undergraduate freshmen are still struggling to master Python is lack of programming experience. (Gressmann et al., 2019) found that on average, about seventy-five percent of students investigated in the universities of ACM International Conference showed no programming experience before enrolling in universities. However, most college instructors assumed their students built CS foundation in high schools, so they taught directly from the algorithms and applications of programming instead of making detailed introduction (Serrano Corkin al., 2020).
According to an investigative report (Code.org, CSTA, & ECEP Alliance, 2020), “the researchers found that only 47% of high schools in United States teach foundational computer science” (p. 2). This means that students without programming language account for the majority. Although AP computer science exam might be a helpful way for high school students to learn the basics of CS, only 160000 students participated this exam in the year of 2019 (Code.org al., 2020). Therefore, most of students experience that lack of opportunities of learning CS before they attend colleges.
This caused that students with programming experience were more likely to pass the course and get a grade while students without programming experience had to spend plenty of extra time and effort in order to keep up with the progress. Bergin (2005) pointed out that first year undergraduate students having difficulty learning computer science could cause high drop-out percentage during the school year and failure (eg. unemployment) rates after graduation, so this should be regarded as a serious problem of practice which requires educators to solve.
Jenkins (2001) hypothesized the relationship between students’ motivation and programming learning through studying intrinsic, extrinsic, social and achievement motivations. This proves that there is educational need to solve this problem. Mastering the knowledge and concepts of Python is the first and most important step for California undergraduate freshmen to pass exams and successfully major in CS. If students fail to understand the contents covered in the course and are unable to apply what they have learned in practice, they will find difficulty completing advanced courses and might be required to retake designated courses.
This negative impact can even prevent California undergraduate freshmen from successfully graduating or gaining a foothold in the CS industry when they pursue career development. In the past few decades, many researchers explored the best approaches to help students improve academic grades of programming learning. The scholars who studied educational psychology and learning behaviors firstly proposed the relationship between motivation theories and programming learning (Vallerand, Koestner, & Pelletier, 2008).
This shows that designing an educational approach according to the theoretical framework of motivation might help increase students’ CS academic scores. After deliberate analysis, scholars found there was connection between students’ programming learning and motivation theories (Jerkins, 2001). This means that it is important to determine which theory plays the most critical role in students’ CS learning. Because designing pedagogical approaches based on extrinsic motivation made achievements in multiple educational cases, many scholars also hypothesized extrinsic motivation should play the critical role in motivating students to learn CS.
This indicates that extrinsically motivating undergraduate students to learn the concepts and knowledge of Python might be an efficient way. Nevertheless, the experiment results showed that lots of educational solutions designed to extrinsically motivate undergraduate students learn CS didn’t work well (Dolgopolovas et al., 2018). This emphasizes that extrinsic motivation is not the factor which plays the critical role in designing an educational solution to help students achieve academic success in Python learning. Therefore, Mastering the knowledge and concepts of Python’s learning needs of California undergraduate freshmen without programming experiences are still not being met.
Although in the past a large number of scholars agreed that extrinsic motivation should play the major role in designing educational solutions to help undergraduate students learn Python, recently research by (Umapathy, Ritzhaupt, & Xu, 2020) proposed another perspective through experiments and investigations. Researchers found that most undergraduate students who decided to take a Python course showed a remarkable amount of extrinsic motivation through questionnaires (Lepper, Corpus, & Iyengar, 2005).
These extrinsic motivations could be summarized into three categories: 1) finding a high-paying job after graduation; 2) satisfying parents’ expectations; 3) previous requirement before transferring major to CS (Glogger-Frey et al., 2015). This indicates that it is meaningless to continue simply designing pedagogical approaches relying on extrinsic motivation to help California undergraduate students learn the knowledge and concepts of Python. However, after studying through control group experiments, researchers found that undergraduate students who could really pass the exams and successfully major in CS showed enough intrinsic motivation (Gopalan et al., 2017).
This points out that intrinsic motivation should be regarded as the factor which plays the critical role in influencing California undergraduate freshmen’s Python learning. After confirming the reliability of this finding through data analysis, an increasing number of scholars begin to study how to help students learn CS by increasing their intrinsic motivation. According to Vallerand et al. (2008), “educational solutions designed according to theoretical frameworks including intrinsic and extrinsic motivation theory, self-determination theory, ARCS (Attention, Relevance, Confidence, and Satisfaction) model, social cognitive theory and expectancy theory have generally achieved successes and supports” (p. 260).
This finding narrowed theoretical frameworks into five. The recent trend represented that the emerging research area was centered in self-determination theory (SDT) because educational solutions designed based on SDT showed the most effective results among students’ CS learning outcomes (Vallerand et al., 2008). This illustrates that SDT should be chosen to design a new pedagogical approach to help students learn Python. Thus, designing an educational solution according to SDT might help California undergraduate freshmen without programming experiences master the knowledge and concepts of Python’s learning needs.
In recent years, a growing number of scholars have started to study how to utilize SDT to design educational solutions to help undergraduate students succeed in CS learning. Self-determination theory (SDT) is developed from the study of intrinsic and extrinsic motivations (Walker, Greene, & Mansell, 2006). This indicates that SDT well combined the advantages derived from intrinsic and extrinsic motivations and the educational solution should also be designed to benefit from both intrinsic and extrinsic motivations.
The researchers found that SDT included not only external controls in extrinsic motivation, but also self-regulation in intrinsic motivation, and both of these characteristics contained positive effects on students’ CS learning (Vallerand et al., 2008). This means that designing pedagogical approaches based on SDT is able to enhance the students’ academic performance and motivation. Furthermore, the following model graph well presented how SDT as the theoretical framework can be referred to design educational solutions.
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Note. Photo by Ackerman (2015)
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To begin with, according to photo by Ackerman (2015), Ryan (2000) pointed out nonself-determined and self-determined on the horizontal axis, and listed motivation, regulatory styles, what is the source of the motivation and what regulates the motivation on the vertical axis. This shows that there should be a transition between nonself-determined and self-determined during the learning process, and it is important to determine the factors on the vertical axis.
Secondly, Ryan (2000) classified motivations into amotivation, extrinsic motivation and intrinsic motivation, and listed four regulatory styles and their corresponding sources of motivation of extrinsic motivation in detail. This means that determining students’ type of extrinsic motivation is very important to design the educational solution to which can efficiently increase the intrinsic motivation to achieve the goal of intrinsic regulation.
Last but not least, Ryan (2000) enumerated possible factors that regulate the motivation for corresponding regulatory styles to construct the relationship presented in this model. This infers that specifying the appropriate regulatory style can efficiently regulate the targeted motivation, so students can finally become self-determined in CS learning. In this way, the educational solution to help California undergraduate freshmen without programming experiences to learn Python should be designed to manage the regulatory style to form the transition from being extrinsically motivated to intrinsically motivated.
According to SDT model and the problem of practice’s context, introjected regulation should be chosen as the regulatory style to design the educational solution to help California undergraduate freshmen without programming experiences to learn Python through the transition from being extrinsically motivated to intrinsically motivated. Although some scholars argued that integrated regulation could be used to design pedagogical solution because the source of the extrinsic motivation was internal, which was assumed to transfer motivation of students’ CS learning from extrinsic to intrinsic, the research results overthrew this hypothesis by showing that it was difficult to inspire students’ congruence and awareness during the process of CS learning (Gopalan et al., 2017).
This also indicates that identified regulation can’t be chosen as regulatory style because the source of motivation is somewhat internal. Moreover, external regulation can’t either be the regulatory style since scholars have proved that undergraduate students presented a large amount of extrinsic motivation in CS learning. On the other hand, introjected regulation supports recent year’s research findings because external motivation is the man source.
In addition, introjected regulation demonstrates feasibility of regulating extrinsic motivation through self-control, internal rewards and punishments to transfer to intrinsic regulation and trigger the positive effects of intrinsic motivation according to contemporary scholarly research’s trend. Consequently, combining introjected regulation as regulatory style and intrinsic motivation to design the educational solution can efficiently help California undergraduate freshmen without programming experiences succeed in Python learning through the transition from being extrinsically motivated to intrinsically motivated.
