According to the Philippines Commission on Higher Education (CHED) Circular Memorandum Order (CMO) 101 series of 2017, one of the allowed Elective Courses for the Electronics Engineering (ECE) Program is Instrumentation and Control which includes Advanced Instrumentation and Control Systems and Robotics. With these elective courses, there will be a need to have designated laboratory facilities and equipment which includes Mechatronics and Automation Equipment. This study aims to bridge the academe-industry gap by integrating a project based learning approach. The students of Jose Rizal University designed and developed a project-based four-story elevator trainer that became a platform for the real-world model of an actual elevator. Basic and Advanced Programmable Logic Controller (PLC) and Human Machine Interface (HMI) programming skills of the students were enhanced as they developed twenty comprehensive laboratory experiments on the elevator trainer. A 94.2% usability result indicates substantial effectiveness in learning mechatronics. T-tests for means and equal variance were used to analyze the significance of the elevator trainer in comparing the pre-test and post-test scores of ECE students. An alpha (p-value of 0.00000000013) for the accumulated test scores of each ECE student and (p
-value of 0.0000291) for the comparison of average mean score on the six areas: PLC, HMI, Variable Frequency Drive (VFD), pneumatics, electropneumatics, and motor control were found to be both smaller than the alpha of 0.05 for the target 95% confidence level. The study showed that the effectiveness of the elevator trainer was statistically significant in the learning of the ECE students in a project-based approach. The study highlights the need for a more hands-on project in each laboratory course to increase the quality of produced electronics engineering students in the Philippines by allowing them to be well-trained, globally competitive, and equipped with the necessary skills to become industry-ready.

The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the overall academic performance evaluation. To address this gap, this study employed cognitive diagnostic modeling (CDM) to profile the skill mastery of programming students. An empirical analysis was conducted to select the most appropriate model for the data, and the linear logistic model (LLM) was determined to be the best fit. Final examination results from 308 information technology (IT) and 279 computer science (CS) students were analyzed using the LLM. Unfortunately, findings revealed that programming students exhibited proficiency primarily in code tracing and language proficiency but displayed deficits in theoretical understanding, logical reasoning, and algorithmic thinking. From a practical standpoint, this deficiency in fundamental skills sheds light on the factors contributing to academic failures and potentially eventual dropout in programming education. When comparing the student population by academic program, CS students demonstrated superior mastery compared to their IT counterparts, although both groups exhibited a lack of mastery in code tracing. These deviations underscore the pressing need for tailored educational strategies that address the unique strengths and weaknesses of each student group. Overall, this study offers valuable insights into programming education literature and contributes to the expanding application of CDM in educational research.

The emergence of generative AI tools like ChatGPT has sparked investigations into their applications in teaching and learning. In computer programming education, efforts are underway to explore how this tool can enhance instructional practices. Despite the growing literature, there is a lack of synthesis on its use in this field. This rapid review addresses this gap by examining the current literature to outline research trends, assess how it supports teaching and learning processes, and discern the issues that emerge from its application in programming instruction. A total of 107 documents disseminated across 81 distinct sources and authored by 394 contributors were identified. The review adopted a broad and inclusive approach, selecting literature based on relevance to ChatGPT's application in programming education and encompassing studies from diverse settings and methodologies. Results highlight applications such as personalized tutoring, knowledge reinforcement, instructional material creation, source code generation, immediate feedback, and assessment support. However, its use also introduces challenges such as academic dishonesty, ethical dilemmas, diminished critical thinking, overdependence on ChatGPT, and various technical limitations. Considering these findings, a balanced approach to the utilization of ChatGPT in programming education is essential. Implications and recommendations have been provided to guide policymakers, curriculum designers, teachers, and students in harnessing the benefits of this technology while mitigating potential challenges.

Computer programming is a difficult course for many students. Prior works advocated for group learning pedagogies in pursuit of higher-level reasoning and conceptual understanding. However, the methodological gaps in existing implementations warrant further research. This study conducted a three-armed cluster-randomized controlled trial to comparatively evaluate the social and cognitive effects of group learning pedagogies in computer programming. Following an apprenticeship model, each group has a designated master: drivers in pair programming (PP), peer leaders in peer-led team learning (PLTL), and practitioners in practitioner-assisted group learning (PAGL). In all course deliverables, the PP group received the lowest mean scores. Meanwhile, no significant difference was found between the PLTL and PAGL groups. Except for psychological safety, social factors such as task cohesion, interdependence, and group potency were significantly different between the groups. Both PLTL and PAGL groups reported a significant increase in social factors after 14 weeks of intervention. These findings provide a rationale for educational leaders and teachers to formulate curricular plans that integrate PLTL and PAGL in computer programming education. Overall, this study contributes to the literature on group learning, expands the pedagogies in computer programming, and serves as additional empirical evidence on cognitive apprenticeship and sociocultural perspectives of learning.

Among computer programmers and developers, the user-oriented question-and-answer website of Stack Overflow is a useful platform for sourcing solutions to programming problems, exchanging insights, and accessing a wealth of shared knowledge. However, the timeliness of responses on this platform is frequently a limiting factor that ChatGPT could potentially address. The goal of this study was to explore the preferences of novice programmers between these platforms for finding answers to their programming questions. Anchored in the Technology Acceptance Model (TAM) and the Information Foraging Theory (IFT), the study investigates users' perceptions of usefulness, ease of use, information scent, cognitive effort, as well as overall preferences. Our findings show discernible variations in preferences within the group of students (i.e., application and website developers). In line with these results, we discussed theoretical and practical implications and suggested a dual-pronged approach to leverage both environments as coding assistants in computer programming education.

Recent studies have acknowledged social media as a valuable pedagogical tool for connecting the formal and informal learning gap. However, as a new platform, the literature is sparse on the potential of TikTok as a knowledge source. In this study, we explored programming TikTok videos in the #LearnProgramming webpage in terms of content (programming languages and topics) and characteristics (video styles and types). Although TikTok is principally an entertainment destination, our results show that the platform likewise has informative videos. The 349 videos that we examined received a total of 10,046,000 views, 10,523 comments, 932,871 likes, and 35,095 shares, implying extremely high levels of user engagement. Tiktokers showing tips and tricks are the most recurring content type. From a macro perspective, we noticed that TikTokers do not follow the ethos of the platform (e.g., dancing) when producing educational content. This deviation demonstrates the intent of TikTokers to educate than to entertain. Although it is too early to conclude that TikTok can operate as a nanolearning platform, we discovered a substantial amount of content for and engagement from programming learners. Our results lay a potent foundation for devising actionable scholastic implications, policies, and recommendations concerning TikTok consumption. Future works and research prospects were also discussed to propel the social media and nanolearning literature forward.

Computer programming is now one of the most critical competencies taught in computer courses. [1]. Students require any assistance they can get when learning programming in order to acquire the necessary abilities to excel in the field of computing [2]. This paper aims to investigate the C compiler error logs of Computer Science freshmen students. A prototype was developed and pilot-tested to obtain C source code snippets focusing on assignment statements. The dataset consisting of 1013 logs were extracted from the initial prototype then followed the data science approach of [3] for pre-processing. A Person correlational analysis was conducted on eight features to investigate the relationship between all variables in the dataset. Results of the study show that there is a strong relationship between wrong expression and operator (0.806), wrong expression and numeric value (0.794), operator and numeric value (0.663). Implications of this study is also helpful to computing instructors to improvise the delivery of their teaching pedagogy.

Following recent advancements in automatic speech recognition (ASR) technologies, we replicated an experiment four decades ago that utilized voice as an input modality for computer programming. We also extended this experiment by investigating the pedagogical effectiveness of ‘programming by voice’ in terms of attitude, self-efficacy, code correctness, and coding speed. A total of 96 students from an institute of technology in the capital region of the Philippines were randomly selected to participate in a quasi-experimental study using a one-group pretest-posttest design. We subjected students to programming activities with different levels of difficulty to compare voice and keyboard. Our results show that although voice decreases negativity, it likewise decreases control, which means that both attitude and self-efficacy are positively and negatively affected, respectively. Using voice as an input modality also allows students to code faster when the activities are easy but not when they are moderate or difficult. Code correctness analysis shows that voice is only preferable for easy and moderate machine problems. With the deviation of our findings from an experiment four decades ago, we can now conclude that ASR technologies and voice as input modality provide substantial implications and new opportunities for teaching and learning computer programming.

Computer programming is one of the fundamental skills in the field of computing [1]. In a computing class, students are expected to learn the skills rather than remembering materials only. This study aims to develop a constraint-based student model (CBM) by analyzing the computing students' C compilation error logs. The proposed modified CBM will be used as input to develop a user behavior of an ongoing study for an intelligent tutoring system. The prototype was developed to obtain compilation error logs from the selected students, it contains five (5) C programming questions that focus on assignment statements. The prototype of the study was pilot tested on two (2) online programming classes with a total of thirty-one (31) freshman college students composed of nine (9) BSCS and twenty-two (22) BSIT participants with a mean age of 18.68, where nineteen (19) or 61.3% are males and twelve (12) or 38.7% are females. The study uses convenience sampling to determine the total number of student participants. The dataset was extracted from the prototype and feature identification was performed on one thousand thirteen (1013) C programming logs which resulted to obtain eight (8) error types. The paper of Khodeir, Wanas, & Elazhary (2018) [2] and Karaci (2018) [3] on constraint-based modeling was reviewed to develop a proposed constraint-based model in the context of C programming focusing on assignment statements. By mapping a student error on the suggested constraint relevance (Cr) and constraint satisfaction, the database for constraints was finished (Cs).

Computer programming education is often delivered using individual learning strategies leaving group learning techniques as an under-researched pedagogy. This pose a research gap since novice programmers tend to form their own group discussions after lecture meetings and laboratory activities, and often rely on peers when a topic or activity is difficult. Thus, this study intends to evaluate the impact of cooperative learning using jigsaw technique when teaching computer programming to novice programmers. A quasi-experimental research using a nonequivalent control group pretest-posttest design was adopted to examine the impact of jigsaw teaching strategy. After a 14-week programming course, pre- and post-test results revealed a significant increase in terms of attitude and self-efficacy, and the experimental group demonstrated significantly higher scores than in the control group. Therefore, it was concluded that cooperative learning using Jigsaw technique is a valid and effective teaching strategy when handling novice programmers in an introductory programming course.