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The current and ongoing challenges brought on by climate change will require future scientists who have hands-on experience using advanced molecular techniques, can work with large data sets, and can make correlations between metadata and microbial diversity. A course-embedded research project can prepare students to answer complex research questions that might help plants adapt to climate change. The project described herein uses plants as a host to study the impact of climate change-induced drought on host-microbe interactions through next-generation DNA sequencing and analysis using a command-line program. Specifically, the project studies the impact of simulated drought on the rhizosphere microbiome of Fast Plants rapid cycling "Brassica rapa" using inexpensive greenhouse supplies and 16S rRNA V3/V4 Illumina sequencing. Data analysis is performed with the freely accessible Python-based microbiome bioinformatics platform QIIME 2.

Many 4-year public institutions face significant pedagogical challenges due to the high ratio of students to teaching team members. To address the issue, we developed a workflow using the programming language R as a method to rapidly grade multiple-choice questions, adjust for errors, and grade answer-dependent style multiple-choice questions, thus shifting the teaching teams' time commitment back to student interaction. We provide an example of answer-dependent style multiple-choice questions and demonstrate how the output allows for discrete analysis of questions based on various categories such as Fundamental Statements or Bloom's Taxonomy Levels. Additionally, we show how student demographics can be easily integrated to yield a holistic perspective on student performance in a course. The workflow offers dynamic grading opportunities for multiple-choice questions and versatility through its adaptability to assessment analyses. This approach to multiple-choice questions allows instructors to pinpoint factors affecting student performance and respond to changes to foster a healthy learning environment.

Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's academic environment and the professional application of their skills in an employment and internship setting. This study examined the design of a data analytics practicum course. The main objectives included (1) the identification of topics and skills employers look for in new hires in data analytics-related internships and entry-level positions, (2) the development and implementation of a Data Analytics practicum course and (3) reflection on the first-time offering of the course and suggested improvements for the next iteration. As part of this study, industry and organization survey responses drove the design of the course and development of key student learning gains for five learning modules throughout the semester. Faculty within the departments of information technology (IT), mathematics, and statistics collaborated in the construction, development, and implementation of team-teaching instructional practices of the Data Analytics Practicum in Spring 2023. This study applies an interdisciplinary approach to data analytics practicum development and instruction.

The academic success of first-year students' learning in science faculties is by no means assured, especially in an Open Distance Learning setting with its limited number of face-to-face encounters between students and lecturers or tutors. Therefore, such encounters should be highly efficient in view of the considerable amount of knowledge transfer to students. The University of South Africa (Unisa) makes provision for contact sessions of 15 hours per semester for selected modules in an attempt to elevate the pedagogical efficiency of these sessions by focusing on the threshold concepts as an innovative way of learning. This paper shows that tutorials adopting the threshold concepts approach have the potential to make students academically more successful. The focus of this study is an introductory information systems module that teaches the Python programming language. Our statistical analysis demonstrated that the year marks and final exam scores of the participating students were frequently higher than those of the students in the control group.

This study tried to design a WhatsApp bot to be implemented in English language vocabulary learning context in Oman. 150 Omani English as a Foreign Language (EFL) students from three different proficiency levels were selected based on random sampling. To measure the effectiveness of the treatment, pretests, posttests, and delayed posttests were conducted, respectively. The TAM 2 extended questionnaire was also used to understand learners' perceptions regarding the use of the WhatsApp bot in the learning process. The results of the study revealed that the WhatsApp bot, on all three levels, works nicely and practically. Although experimental groups, who used WhatsApp bot, at elementary and intermediate levels showed significant differences from the traditional face-to-face classes, pre-intermediate students showed no significant difference between the two mentioned terms. It was also understood that chatbots could be the best supplementary materials assisting teaching in delivering materials.

The current study attempted to measure the impact of using an interactive WhatsApp bot designed using Python language programming in grammar learning. To this end, sixty Omani pre-intermediate English proficiency learners were the sample population of this study to act as a control and experimental group, with an equal number of students in each group. A pretest was conducted to measure students' grammatical knowledge and ensure the homogeneity of the students before treatment. Although both groups received in-class instructions on using grammar, the experimental group received treatment that used an interactive WhatsApp bot as an extra facilitator to obtain materials outside of the classroom. After the treatment period, a posttest was conducted to compare the results of the two groups. Three weeks later, a delayed post-test was conducted to check the students' retention of the grammatical structure. The study's findings showed that both groups progressed in the posttest, which could result from teachers` instructions; however, the experimental group showed statistically significant progress in the posttest compared to the control group. The results of this study could provide better opportunities for teachers, students, and institutions to work efficiently and accurately in an academic environment.

In the face of the challenges posed by the COVID-19 pandemic, the hybrid teaching model has garnered significant attention for its combination of the depth of traditional education with the convenience of distance learning. Focusing on the domain of computer programming language instruction, this study innovatively designs a hybrid teaching strategy aimed at fully exploiting the flexibility of its teaching design and the variety of pedagogical approaches. The strategy integrates face-to-face teaching with online autonomous learning, incorporating project-based teaching methodologies and immediate feedback mechanisms to facilitate active student engagement and deep learning. Through a year-long practice in a C++ programming course, encompassing 68 students, the study empirically validates the effectiveness of the hybrid teaching approach. It not only demonstrates remarkable educational outcomes, enhancing the quality of programming instruction and student satisfaction with their learning experience, but also employs Bayesian analysis to delve into the relationship between learning trajectories and students' sense of self-efficacy. By focusing on key indicators during the learning process, such as the timeliness and quality of online learning, laboratory work, and project assignments, the study then utilizes Bayesian models to directly assess the impact of these learning behavior metrics on students' perceived self-efficacy. The findings reveal that students with outstanding academic achievements exhibit higher levels of self-efficacy, confirming that academic performance can reasonably reflect teaching effectiveness and provide a quantifiable basis for assessing individual learning progress. Consequently, this research not only contributes a novel strategy to computer programming education practice but also offers a valuable reference for the application of hybrid teaching models in other disciplines. Furthermore, it promotes in-depth contemplation on post-pandemic innovations in teaching modes and issues of educational equity, laying a solid foundation for constructing a more adaptive and inclusive future education system.

Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for "playing" with concurrency, and "hearing" common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students' misconceptions regarding concurrency in Sonic Pi and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi's simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

Effect sizes are important because they are an accessible way to indicate the practical importance of observed associations or differences. Standardized mean difference (SMD) effect sizes, such as Cohen's d, are widely used in education and the social sciences -- in part because they are relatively easy to calculate. However, SMD effect sizes assume normally distributed data, whereas most data in these fields are ordinal and/or non-normal. In these situations, SMD effect sizes can be biased, and a non-parametric measure such as Cliff's [delta] is more appropriate. This paper provides a practical guide on how to calculate Cliff's [delta]. First, we present a conceptual overview and a worked example. Then we present two methods of calculating Cliff's [delta]: (1) a web-based Shiny application developed to accompany this paper (https://cliffdelta.shinyapps.io/calculator; suitable for all users), and (2) an R tutorial (suitable for R users). This is intended to provide researchers and practitioners with an appropriate and accessible effect size measure for non-normal data.

The procedures of carrying out factorial invariance to validate a construct were well developed to ensure the reliability of the construct that can be used across groups for comparison and analysis, yet mainly restricted to the frequentist approach. This motivates an update to incorporate the growing Bayesian approach for carrying out the Bayesian factorial invariance, as well as the frequentist approach, using the recent add-on R packages to show the procedures systematically for testing measurement equivalence via multigroup confirmatory factor analysis. The practical procedure and guidelines for carrying out factorial invariance under MCFA using a classic empirical example are demonstrated. Comparison between the frequentist and the Bayesian procedures and demonstration using priors are another two nuclei of the paper.

This study explores the application of the Project-Based Learning with Gamification Model (PBLGM) through Visual Programming Language (VPL) to enhance digital competencies and problem-solving skills in learners. The PBLGM model integrates project-based learning and gamification techniques using Kodu Game Lab, aiming to develop essential 21st-century skills. The research involved designing, developing, and evaluating the PBLGM model. Participants included 30 undergraduate learners from Rajamangala University of Technology Tawan-Ok. The study's findings indicated significant improvements in digital competencies and problem-solving skills post-intervention. The consistency index values ranged between 0.40 and 1.00, with an average value of 0.84. The difficulty values ranged from 0.38 to 0.57, and the reliability value (KR-20) was 0.83. The model effectively enhanced digital competencies and problem-solving skills, as evidenced by improved test scores and positive expert evaluations. This study underscores the importance of integrating gamification and project-based learning in educational contexts to foster critical digital skills.

Due to swift technological changes in society, programming tasks are proliferating in formal and informal education around the globe. However, challenges arise regarding the acquisition of programming skills. Many students are unequipped to develop programming skills due to limited instruction or background and therefore feel insecure when encountering programming in higher education. Some after-school initiatives focus on teaching younger students programming skills, however, not all students have the opportunity to attend. It can also be very challenging for teachers to teach programming--even more so due to significant differences in students' knowledge and interests. To alleviate these challenges, we designed and developed a digital personalized learning (DPL) track for programming in the first grade of secondary education (12-14 year-old students) with a threefold purpose: (a) to encourage students bridging the gap between visual and more general-purpose textual programming languages (b) to meet differences in students' programming knowledge by challenging them, albeit on their own pace, and subsequently (c) to support teachers in the delivery of programming education with relevant supportive learning materials. The design was tested by students and teachers, both of varying technical abilities. Assessments of the DPL-track were positive, with students identifying the tasks as challenging and the tools as motivating. Teachers praised the adaptivity, as well as the gradual transition from visual to textual programming. We present several suggestions for design improvement and dilemmas while reflecting on our design case.

This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already know, then submit their solutions, and get automatic feedback from the system. We analyzed quantitatively how students used the system by inspecting the logged actions of the students using the system. We also did a qualitative analysis by interviews, to find out how the students appreciated the use of the repository and to get feedback for improvements. We focused on how students select exercises as finding the exercise that fulfils the training needs of a student is the innovative part of our repository.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in the era of big data. Learning intervention, as a core concept of learning analytics, refers to the purposeful and planned adoption of direct or indirect strategies or behaviors based on tracking learning behaviors and integrating information about learners' characteristics to give learners personalized guidance and assistance in order to help learners break through the status quo of learning difficulties and improve their learning abilities, so as to achieve tailored teaching. In this study, data mining was conducted on the performance records of students on math problems in an online learning system, and a learning intervention suggestion prediction model was constructed on the basis of decision tree algorithm using Python, with a view to understanding the effectiveness, willingness, style, and other characteristics of the learners' online learning through the analysis results, providing personalized guidance to students, and enabling teachers to intervene with at-risk students and successfully complete the teaching goals. It was found that the most significant impact of the learning intervention advice provided to learners was the number of hints they sought during the learning process, and that learners who needed to be "intervened" or "monitored" could be categorized into two groups: independent inefficient and dependent inefficient according to the model. Therefore, teachers or adaptive learning systems should intervene in a timely and appropriate way for different types of learning crisis groups to solve the problems of poor learning performance, insufficient commitment to learning, poor learning habits, low participation in learning, low self-efficacy and other problems of learners in different learning scenarios.

Understanding students' learning of knowledge components (KCs) is an important educational data mining task and enables many educational applications. However, in the domain of computing education, where program exercises require students to practice many KCs simultaneously, it is a challenge to attribute their errors to specific KCs and, therefore, to model student knowledge of these KCs. In this paper, we define this task as the KC attribution problem. We first demonstrate a novel approach to addressing this task using deep neural networks and explore its performance in identifying expert-defined KCs (RQ1). Because the labeling process takes costly expert resources, we further evaluate the effectiveness of transfer learning for KC attribution, using more easily acquired labels, such as problem correctness (RQ2). Finally, because prior research indicates the incorporation of educational theory in deep learning models could potentially enhance model performance, we investigated how to incorporate learning curves in the model design and evaluated their performance (RQ3). Our results show that in a supervised learning scenario, we can use a deep learning model, code2vec, to attribute KCs with a relatively high performance (AUC > 75% in two of the three examined KCs). Further using transfer learning, we achieve reasonable performance on the task without any costly expert labeling. However, the incorporation of learning curves shows limited effectiveness in this task. Our research lays important groundwork for personalized feedback for students based on which KCs they applied correctly, as well as more interpretable and accurate student models.

Python programming is widely employed in educational institutions worldwide. Within the "Merdeka Belajar" curriculum context, this programming is recognized as a suitable vehicle for mathematics instruction, significantly influencing students' motivation and learning outcomes, particularly following periods of educational hiatus. This study examines the effectiveness of Python programming in promoting heightened learning outcomes by examining the intricate relationship between student motivation and learning. The study uses quantitative research methodologies to evaluate student learning facilitated through Python programming, encompassing problem-solving assessments and the administration of motivation questionnaires. By engaging in coding practices, students understand the symbols they manipulate, facilitating their ability to juxtapose data derived from mathematical modeling with the resultant programming output. When disparities arise, students are empowered to reassess their work, fostering a more profound comprehension of the subject matter. These exercises serve to augment students' capacity to retain and process information within memory. Furthermore, students demonstrate a favorable disposition, exhibiting persistence in resolving programming challenges by meticulously analyzing error outputs, particularly those pertaining to TypeErrors. Encouraging students to confront errors through thoroughly examining error output manifestations engenders an efficacious learning paradigm. This research proffers invaluable insights for educational institutions contemplating the integration of Python programming as an instructional adjunct.

Computational thinking (CT), an essential competency for comprehending and addressing intricate issues in the digital world, has been incorporated into curriculum planning as a goal for programming education. This study introduced flow design into programming curricula to investigate its impact on undergraduates 'CT skills during pair work. Two types of flow design approaches, construct-by-self flow design (CBS-FD) and construct-on-scaffold flow design (COS-FD), were proposed to determine which approach better enhances students' CT skills. Seventy-six first-year undergraduates participated, including thirty in the CBS group and thirty-six in the COS group. Evaluations made from the results and processes of programming tasks were employed to describe computational performance and computational practices, respectively. Data gathered from CT skill surveys were thoroughly analyzed to gain a deeper understanding of computational perspectives. Our findings highlighted that COS-FD significantly improved participants' computational performance compared with CBS-FD. The COS groups fostered an engaging, sharing atmosphere, while CBS groups spent more energy on negotiating the manipulation of flow design. Moreover, both COS-FD and CBS-FD proved beneficial in enhancing participants' computational perspectives, with the COS groups better improving their algorithm thinking. The study presents valuable perspectives on the design and implementation of collaborative programming activities within curriculum education.

The process of diffusion is intimately related to random molecular displacement and is central to many problems in chemistry. Here, we provide an intuitive derivation of the diffusion equation based on the analysis of the random trajectories of particles generated and plotted by simple MATLAB and Python scripts. The codes are very simple and profusely commented on, so they can be modified by students and instructors with little experience with these software packages. The codes plot the trajectories in 1D, 2D, and 3D of a number of particles during a number of steps, both specified by the operator. Advanced versions of the codes with animated evolution of the diffusion process are also provided. The interactive generation and analysis of the trajectories of the particles in different scenarios allows a faster and more complete understanding of the effect of the number of particles, diffusion time, or dimensionality of the system, on the basic aspects of chemical diffusion, like mean square displacement, through the statistical nature of the process. A series of activities for the instructor and students was also suggested.

This study reports on the usefulness of computer skills for enhanced teaching and learning among lecturers in an open-distance e-learning (ODEL) environment. Computer skills have become fundamental due to increasing new technological tools of web-based management systems, laptops, digital tablets, learning management systems, webinars, and the applications of Microsoft Teams, Zoom, and WhatsApp manufactured daily. The quantitative research approach was employed in this study where data was collected through an online questionnaire from respondents in two ODEL institutions. The purposive and snowball sampling technique was applied to enable reaching out to lecturers with whom the author has contact in the two ODEL institutions. First, the author purposively approach those in his contacts, before requesting those lecturers he contacted to help spread the good news of his research investigation to other of their contacts. The study sampled 80 lecturers from both countries' ODEL institutions leading to sending out 80 copies of the online questionnaire to the lecturers. Of the 80 online questionnaires sent, only 50 were received. The results obtained were analyzed using Microsoft Excel presented in frequency and percentage. Findings reveal that the purpose of computer skills is to enhance teaching and learning among Lecturers in the ODEL environment between the country's institutions. Exposition to programming languages and applications, ability to navigate within the online platform, use of communication/networking tools of social media of Facebook, Twitter, and teleconferencing tools of Zoom, Skype, Webinar, Webcast, Teams, WhatsApp) were profoundly enabled through computer skills. Findings further indicate that computer skills have influenced lecturers for personal development in multifarious ways, user experience, memory usage, and storage of information, ease students grading, and developing course materials, facilitate instructional delivery of courses/modules taught at a geometric progression, and support lecturers using the asynchronous and synchronous method in teaching and learning. Based on the findings, the study recommends upskilling lecturers to advance their knowledge in computer skills through continual use of new technologies for better and quality services in teaching and learning to meet students' information needs.

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

Human-driven carbon emissions have resulted in increased levels of dissolved carbon dioxide in the Earth's oceans. This dissolved carbon dioxide reacts with water to form carbonic acid, which impacts ocean acidity as well as the solubility of carbonate-containing compounds, with far-reaching impacts on marine ecosystems and the human communities that depend on them. Exploring these connected chemical processes offers a rich opportunity to connect chemistry to current environmental issues while illustrating how to quantitatively approach equilibrium in complex chemical systems. Generally, nonlinear algebraic methods are used to determine the equilibrium concentrations of chemical species in systems with multiple equilibrium processes. Because these methods are cumbersome to solve manually and may be unfamiliar to chemistry students, we designed an activity that introduces students to the use of Python coding for solving systems of nonlinear equations. Students wrote code to calculate pH as a function of PCO2 in a freshwater system and compared their Python calculations to experimental pH measurements made on a system of deionized water pressurized with carbon dioxide. An instructor-supplied Python notebook in Google Colaboratory enabled students to further their learning by exploring the effect of PCO2 on model marine systems in which temperature, salinity, and calcium carbonate were added as variables. To support adoption of the Python notebooks for students and instructors with differing levels of coding experience, we provide multiple stand-alone resources that teach the basic coding tools needed to solve equilibrium problems.

The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional e-learning environments deliver instructional videos to the learners by assuming all learners have a single learning preference. Moreover, existing learning style models need to address the recent requirements of e-learning paradigms. To address this issue, this paper presents a novel learning style model tailored for instructional video-based programming e-learning environments that map individual learning preferences with various video design patterns. An adaptive e-learning environment was employed to assess the effectiveness of the proposed model that leveraged a decision tree classifier to divide learners into four preferences. In a paired experimental design, 195 first-year undergraduate students were randomly assigned to one of three groups where learner scores and feedback were taken as evaluation metrics. The control group partook without instructional videos for the entire semester of six months. During the same period, experimental group-1 learned with a traditional video-based e-learning environment, and experimental group-2, with the proposed learning style model, enabled an adaptive e-learning environment. Based on the proposed decision tree learning model, it is understood that the intervention group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver instructional videos based on learners' learning preferences.

Earning Open Badges instead of regular grades and credits can be a motivating factor for high-performing students in terms of attending classes and completing assignments in extracurricular courses, but to what extent? And for what student profiles? To tackle these questions, we conducted a quantitative study with high-performing students. Each student involved in the study had consecutively attended two Java programming courses--one where credits and regular grades were issued for their achievements and performance in the course, and another extracurricular one where Open Badges were issued instead. The study compared the achievement goal orientation (AGO) of each student in the two courses (Wilcoxon paired test). It also examined how students' AGO scores in the Open-Badges-only course were associated with class attendance, completion of assignments and public display of their achievements (badges)--both as individual correlations with these variables (Spearman method), as well as associations with student profiles based on these variables (identified with Ward hierarchical clustering). The results indicate that high-performing students feel less motivated in terms of outperforming/under-performing others and have less fear of not learning enough if they receive Open Badges rather than regular grades. Also, a small portion of high-performers will be fully engaged in an Open-Badges-only course (attendance/completing assignments), while the majority will attend but complete a few assignments or just attend. Still, their AGO is not correlated with attending classes, completing assignments and displaying badges.

This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging due to the potential abstraction of multivariate datasets, and especially when students have a minimal background in statistics or data science. This method aims to help teachers bridge the gap between basic descriptive statistics and the more advanced concepts of PCA; this is done by disregarding mathematical optimization, while emphasizing the use of summary tables and the programming language R. The focus is on implementing this method in an introductory tertiary data science course; however, it may potentially be used in higher level courses, and across a variety of disciplines.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives: To further compare programming effects between and within text-based and block-based modalities, this research conducted a quasi-experimental research in China's secondary school. Methods: An online programming platform, Code4all, was developed to allow learners to program in text-based and block-based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag-sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions: The results indicated that learners in text-based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block-based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications: Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process-oriented and summative analysis to reveal learners' programming learning quality.

Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design to address this gap, involving 64 secondary school students in two groups. For the beginning five weeks, the first group used an introductory block-based programming environment, while the second group used an introductory text-based programming environment. Then, both groups transitioned to professional text-based programming for the subsequent eight weeks. The results showed that participants who transitioned from introductory text-based programming to professional text-based programming (1) significantly outperformed in computational thinking skills; (2) had more code-writing and debugging behaviors and fewer irrelevant behaviors, and (3) had more interactions with the instructor. No significant differences were observed between the two groups regarding enjoyment, confidence, and interest in programming. Drawing on these findings, this study proposes pedagogical implications that could facilitate the adoption of programming modalities within the broader context of STEM education.

The information society is part of current life, and algorithmic thinking and programming are relevant for everybody regardless of educational background. Today's world needs professionals with computing competencies from WEIRD (Western, Educated, Industrialized, Rich, and Democratic Societies) and non-WEIRD contexts. Traditional programming languages include syntax barriers that complicate their overall adoption and usefulness for people from a non-WEIRD context. To solve it, block-based programming languages like Scratch permit the development of programming competencies without syntax restrictions in online environments. This article presents empirical evidence of the positive impact of Scratch with the CARAMBA recommendation system for lessons and exercises proposals based on collaborative filtering of personalized learning from students' experiences. Previous experiences demonstrated that students require assistance in successfully defining sub-competencies and exercises to develop programming competencies by applying Scratch. This work shows the application of Scratch and CARAMBA in a non-WEIRD school context for developing programming competencies. Obtained results show that developing exercises with Scratch and CARAMBA motivated students' autonomy, and as well, the programming learning application increased exam scores in all the analyzed grades. Those results encourage us to continue using Scratch and CARAMBA for developing programming competencies in similar non-WEIRD contexts.

In this study, the influence of socio-cultural factors on attitudes toward artificial intelligence (AI) was investigated. In total, 1,677 Korean middle school students were selected to participate, and a test tool was used to measure the attitude toward AI. As a result, according to socio-cultural factors, middle school students' attitudes toward AI were affected differently by gender- or AI-related experiences. In particular, students experiencing difficulties because of socio-cultural factors showed a more positive attitude toward AI if they had an AI education. On the other hand, interest toward AI and programming experience had a significant effect on attitudes toward AI and was not affected by socio-cultural factors. In particular, students with high interest toward AI or experience with block- and text-based programming languages showed significantly positive attitudes toward AI. Hence, the disparity in middle school students' attitude toward AI according to socio-cultural factors was found.

Bibliometric mapping is widely used in educational technology research to visualize research field development (e.g. the current status and trend). However, there has been limited research examining the present state, challenges, and potential applications of bibliometric mapping techniques in the field of educational technology. In an effort to bridge this knowledge gap, this study conducted a systematic review of 71 articles on bibliometric mapping sourced from 24 journals specializing in educational technology. This review serves as a comprehensive analysis (i.e. a meta-review) of existing reviews in the field. The review focused on productivity, research topics, and research norms. The following key findings were obtained in the study: (1) The field has seen rapid development, with notable author groups, core journals, and major publishing countries; (2) Bibliometric mapping is mainly used for quantitative analysis in five research topics, including specific journals, emerging technologies, learning environments, online and distance learning, and subject concepts; (3) Statistics and evaluations of tools including Bibliometrix package of R language and VOSviewer, databases such as WoS and Scopus, as well as the procedural aspects of the research undertaken, were all considered. Additionally, the study investigated suggested benchmarks for these methods -- investigating the availability of recognized reporting guidelines like PRISMA or others, as well as the presence of specific criteria for determining what should be included or excluded when utilizing bibliometric mapping techniques. The study proposed standards for the application of bibliometric mapping techniques in research, accompanied by relevant examples. This study not only reveals the specific value and current applications of bibliometric mapping techniques in educational technology research but also provides clarity on the specific methodologies for their implementation and identifies potential areas for further exploration. Finally, this study puts forth a number of recommendations aimed at fostering the reasoned utilization of bibliometric mapping techniques in the field of educational technology.

Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this article, we demonstrate extraction of a problem space through text mining in R. Text mining, an artificial intelligence form of natural language processing, synthesizes and summarizes vast quantities of verbal data. Text mining provides visualization of large narrative datasets to illustrate the structure and connections within the problem space of an ill-structured problem. The Gates Open Research data set of 11,979 verbal autopsy responses (Flaxman et al., 2018) informs the ill-structured problem space of childhood death from infectious disease in developing nations. In this article we apply text mining to automate the process of identifying connections that efficiently illustrate this problem space.

Teaching computer programming language remotely presents particular difficulties due to its requirement for abstract and logical thinking. There is a dearth of research specifically examining the potential factors that determine student performance when distance self-learning is conducted for programming language education. This study aims to determine the influencing factors to conduct successful programming education on distance self-learning to help novice students effectively. The study is underlined by a concept map-based approach. A quasi-experimental approach was used to collect data from seventy-eight students. By utilizing factor analysis, this study proposes nine determinant factors of distance self-learning performance, namely educational architecture, knowledgeability, expectation insight, engagement tools, good organization, technological accessibility, assignment familiarity, advantageous tool, and assistance feature. This work suggested determinant factors that can predict student learning performance to motivate meaningful distance self-learning and construct their knowledge in programming education.

Systematic reviews (SRs) have an important role in the healthcare decision-making practice. Assessing the overall confidence in the results of SRs using quality assessment tools, such as "A MeaSurement Tool to Assess Systematic Reviews 2" (AMSTAR 2), is crucial since not all SRs are conducted using the most rigorous methods. In this article, we introduce a free, open-source R package called "amstar2Vis" (https://github.com/bougioukas/amstar2Vis) that provides easy-to-use functions for presenting the critical appraisal of SRs, based on the items of AMSTAR 2 checklist. An illustrative example is outlined, describing the steps involved in creating a detailed table with the item ratings and the overall confidence ratings, generating a stacked bar plot that shows the distribution of ratings as percentages of SRs for each AMSTAR 2 item, and creating a "ggplot2" graph that shows the distribution of overall confidence ratings ("Critically Low," "Low," "Moderate," or "High"). We expect "amstar2Vis" to be useful for overview authors and methodologists who assess the quality of SRs with AMSTAR 2 checklist and facilitate the production of pertinent publication-ready tables and figures. Future research and applications could further investigate the functionality or potential improvements of our package.

As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators "p-hack" results "within" their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting "right-truncated meta-analysis" (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS.

In the current era where computational literacy holds significant relevance, a growing number of schools across the globe have placed emphasis on K-12 programming education. This field of education primarily comprises two distinct modalities--the block-based programming modality (BPM) and the text-based programming modality (TPM). Previous research may not have provided a complete understanding of the differences between these two modalities as it did not take into account both the learning process and learning outcomes. This study aimed to compare secondary students' programming behaviors, computational thinking skills, and attitudes toward programming between the two modalities through a quasi-experimental design in a Chinese secondary school. The findings showed that (1) learners in TPM encountered more syntactical errors and spent more time between two clicks of debugging, while learners in BPM had more code-changing behaviors by adjusting programming blocks, made more attempts of debugging, and had more irrelevant behaviors; (2) learners in BPM achieved a higher level of computational thinking skills; (3) learners in both modalities experienced a slight decrease in confidence and enjoyment, while learners in BPM had higher interest levels in programming. (4) Code Changer, Minimal Debugger, Maximal Debugger, Distracted Coder and Average Coder were identified through students' programming behavior in the two programming modalities, and differences in their CT skills and attitudinal data were revealed. Lastly, pedagogical implications based on the findings are also discussed.

Generative Artificial Intelligence (AI), especially machine learning models that autonomously generate human-like content, has recently attracted significant attention in the education sector. This paper explores the potential of generative AI, including tools like ChatGPT, to shift from traditional outcome-oriented educational practices to a more balanced approach that values both the learning process and its outcomes. Traditionally, education has emphasized achieving predefined results, but the advent of generative AI tools, which enable students to easily produce tangible results, calls for a reevaluation of these practices. This shift suggests a need for a broader focus that encompasses the entire learning process leading to the final product, thereby promoting an educational practice that equally emphasizes both the journey and the destination of learning. Recognizing that the implementation of such practices, facilitated by generative AI, still requires exploration, this paper proposes a solution that integrates the experiential learning cycle and learning portfolio. This approach is designed to demonstrate the realization of process-outcome-balanced educational practices through the use of a pedagogical AI agent.

Background: Engineering curricula are built around faculty and accreditors' perceptions of what knowledge, skills, and abilities graduates will need in engineering careers. However, the people making these decisions may not be fully aware of what industry employers require for engineering graduates. Purpose/Hypothesis: The purpose of this study is to determine how industry employer-sought professional and technical skills vary among engineering disciplines and levels of education. Design/Method: Using a large sample (n = 26,103) of mined job advertisements, we use the O*NET skills database to determine the frequencies of different professional and technical skills for biomedical, civil, chemical, electrical, environmental, and mechanical engineers with bachelor's, master's, and PhD degrees. Results: The most frequently sought professional skill is problem-solving; the most frequently sought technical skills across disciplines are Microsoft Office software and computer-aided design software. Although not the most frequently requested skills, job advertisements including the Python and MATLAB programming languages paid significantly higher salaries than those without. Conclusions: The findings of this study have important implications for engineering program leaders and curriculum designers choosing which skills to teach students so that they are best prepared to get and excel in engineering jobs. The results also show which skills students can prioritize investing their time in so that they receive the largest financial return on their investment.

Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap. Examining online advertisements to determine the skills sought by employers of new hires for these emerging areas and ensuring that program course content addresses these skills can be a daunting task. In this paper, the authors describe the development of a data-driven analytics framework that can be used for evaluating emerging skill clusters in online job adverts and the application of the framework to a mobile computing course at the authors' institution.

Instruction in an introductory programming course is typically designed to introduce new concepts and to review and integrate the more recent concepts with what was previously learned in the course. Therefore, most exam questions in an introductory programming course require students to write lines of code that contain syntactic elements corresponding to the programming concepts covered during the instruction. This study investigates the number of concepts involved in the exam problems of an introductory Java programming course. In addition, this study compares how the increase in the number of concepts correlates with the ability of students to write error-free lines of code. The instructional method adopted in this study focuses on providing students with a problem-solving schema and a resultant programming plan that integrates many concepts to meet the problem's goal. Results from this study indicate that as the course progresses through the semester, students, on average, apply appropriate problem-solving schemas and programming plans to produce more error-free lines of code, despite an increase in the concept count in the problems. Furthermore, the exam problems later in the course repeat the application of cluster concepts that have appeared in the past exam. This paper illustrates how programming is a cumulative skill and that repeating and building upon the applications of these concept clusters several times through the course increases the likelihood that students will produce more correct lines of code as the semester progresses.

The documentation of test takers' achievements has been accomplished through large-scale assessments to find general information about students' language ability. To remove subjectivity, Cognitive Diagnostic Assessment (CDA) has recently played a crucial role in perceiving candidates' latent attribute patterns to find multi-diagnostic information rather than single proficiency classification. However, there are some gaps in the literature about in detail investigation of test takers' listening comprehension language ability in responding to placement test items of a public English language center. The present study aims to validate an English placement test at a language center through a retrofitting process. In an exploratory mixed-method design, 449 participants from the same language center, including 274 females and 175 males, were selected. The performance of randomly selected participants in a language center placement test was analyzed by applying the GDINA model from R-studio packages, to detect Differential Item Functioning (DIF). Results of the study revealed DIF in some items since there is some bias in test items. The implication of this study is to provide meaningful interpretations of respondents' attributes and improve teaching and learning by finding the strengths and weaknesses of candidates. For this purpose, the findings derived from the result of the study can raise the awareness of test developers in preparing unbiased items for the placement test, and at the same time, assist test-takers to become more critical of their English language achievements. It is also helpful for materials developers to become aware of developing materials free from bias.

Few studies have analyzed students' teamwork (pairwork) habits in programming projects due to the challenges and high cost of analyzing complex, long-term collaborative processes. In this work, we analyze student teamwork data collected from the GitHub platform with the goal of identifying specific pair teamwork styles. This analysis builds on an initial corpus of commit message data that was manually labeled by subject matter experts. We then extend this annotation through the use of self-supervised, semi-supervised learning to develop a large-scale annotated dataset that covers multiple course offerings from a second-semester CS2 course. Further, we develop a series of predictive models to automatically identify student teamwork styles. Finally, we compare trends in students' performance and team selection for each teamwork style to see if any of them reflected better student outcomes or different trends of help-seeking among students. Our analysis showed that applying self-supervised semi-supervised methods helps us to label larger subsets of data automatically and maintains and even sometimes improves the performance of the fully supervised models on a held-out validation set. Our analysis also showed that members of teams in which all members have significant contributions tend to have better performance in class, but their help-seeking behaviors are not significantly different.

This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.

Researchers often have hypotheses concerning the state of affairs in the population from which they sampled their data to compare group means. The classical frequentist approach provides one way of carrying out hypothesis testing using ANOVA to state the null hypothesis that there is no difference in the means and proceed with multiple comparisons if the null hypothesis is rejected. As this approach is not able to incorporate order, inequality, and direction into hypothesis testing, and neither does it able to specify multiple hypotheses, this paper introduces the informative hypothesis that allows more flexibility in stating hypothesis testing and is directly targeted to address and state the researcher's study concern. The two new hypothesis terms under the informative hypothesis framework, the unconstrained and complementary hypotheses are introduced, and the approaches to state the level of evidence using the Bayes factor and Generalization AIC are elaborated. As this hypothesis conception is relatively new and the literature was mostly technical, the main aims of the paper are to introduce this conception, offer a general guideline, and provide an easy-to-read approach to the procedure with practical examples of carrying out this hypothesis approach and contrast it to the frequentist, using the R package.

We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics -beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a block-based programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students' work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.