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An online integrated programming platform to acquire students' behavior data for immediate feedback teaching
Abstract
Programming‐related courses prefer to put importance on practice rather than theoretical knowledge. However, the students' programming behavior data is difficult to be recorded and fed back to teachers anytime, allowing teachers to adjust their teaching in real‐time. Aiming at this problem, this study develops an online integrated programming platform that provides a unified programming environment for teachers and students and logs students' procedural programming behavior data automatically to assist teachers in providing real‐time feedback teaching. To verify the effectiveness of the data‐driven immediate feedback teaching method, an exploratory case was conducted in the 2020/2021 academic year autumn semester. As the control group, cloud201 adopted the traditional hybrid online/offline mode, while cloud202, as the experimental group, adopted the hybrid online/offline mode and immediate feedback teaching mode. The effect is demonstrated through the questionnaire, the programming behavior data analysis, and the performance analysis. The results of the questionnaire show that there are significant differences in the students' satisfaction with interaction, supervision, feedback, and evaluation, of which feedback is the biggest part. In addition, the two analysis results indicate that students' individual learning development and the learning status of the class are objectively characterized, the overall grade distribution of cloud202 is more concentrated in medium grades and the difference on average scores between cloud201 and cloud202 is gradually increased. Since the students' immediate and comprehensive programming behavior data is perceived by teachers, the teaching adjustment is so flexible that the improvement of teaching efficiency and teaching effect is promoted accordingly.
... Meanwhile, the researcher asserts that learners' programming behaviours effectively predict their programming achievement (Araya et al. 2022). Positive behavioural patterns are more likely to lead to improved programming achievement for students (Yu et al. 2023). Therefore, this study concludes that enhancing students' self-efficacy and programming behaviours in collaborative programming is crucial for improving their programming achievement. ...
Background
Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance.
Objectives
This study customises a three‐stage fade‐out scaffolding in collaborative programming to reduce scaffolding dependence and improve students' programming achievement, self‐efficacy and programming behaviour.
Methods
A quasi‐experimental study lasting 18 weeks was conducted at a middle school. One hundred twenty‐one eighth‐grade students participated in the study; they were randomly assigned into the experimental group with three‐stage fade‐out scaffolding (N = 62) and the control group with conventional scaffolding (N = 59). Then, this study adopted an analysis of covariance (ANCOVA), independent samples t test, and lag sequential analysis to analyse the data.
Results and Conclusions
The results indicated that the programming achievement, self‐efficacy, and programming behaviour patterns of the students in the experimental group outperformed those of the control group. Additionally, boy‐dominated groups display more positive and active programming behaviour patterns than girl‐dominated groups.
Implications
This study designs a three‐stage fade‐out scaffolding approach for collaborative programming and provides diverse empirical evidence, offering valuable suggestions for the design of programming instruction and the analysis of learning processes.
In spite of much recent theorizing about teacher provision of feedback, relatively fewer studies look at the dynamic relationships between teacher feedback practices, student feedback experience, and their learning outcomes in higher education settings. To fill this gap, this study looked at 308 university students' perceived teacher feedback practices and their feedback experiences in an English Studies course context at a key and non-key university, and explored how teacher feedback, student feedback motivation and feedback behavior were associated with students' course satisfaction and course exam performance. Results showed that students from the key university reported a higher level of teacher feedback use as well as student feedback motivation and behavior. Structural equation modeling (SEM) suggested that in the case of the non-key university, student feedback behavior significantly predicted course satisfaction and course exam results; teacher feedback also indirectly influenced course satisfaction and course exam results. In the case of the key university, while teacher feedback and student feedback behavior each had significant influence on course satisfaction, student feedback behavior showed no direct significant effect on course exam results, and teacher feedback also showed no significant indirect influence on course exam results.
Distance education (DE), which has evolved under the wings of information technologies in the last decade, has become a fundamental part of our modern education system. DE has not only replaced the traditional education method as in social sciences and lifelong learning opportunities but also has significantly strengthened traditional education in mathematics, science, and engineering fields that require practical and intensive study. However, it is deprived of supporting some key elements found in traditional educational approaches such as (i) modern computer laboratories with installed special software suitable for the student’s field of interest; (ii) adequate staff for maintenance and proper functioning of laboratories; (iii) face-to-face technical support; (iv) license fees. For students to overcome these shortcomings, a virtual application pool is needed where they can easily access all the necessary applications via remote access. This research aims to develop a platform-independent virtual laboratory environment for DE students. This article has been developed specifically to guide DE institutions and to make a positive contribution to the literature. Technology Acceptance Model (TAM) has been used to explain student behaviors. It was concluded that students using the platform performed more successful grades (12.89%) on laboratory assessments and that the students using the developed platform were found to be more satisfied with the education process.
Featured Application
A novel tool to successfully bolster underpinning concepts and techniques in robotics courses in the context of STEM education.
Abstract
Jupyter notebooks are recently emerging as a valuable pedagogical resource in academy, being adopted in educational institutions worldwide. This is mainly due to their ability to combine the expressiveness of traditional explanations from textbooks, with the interaction capabilities of software applications, which provides numerous benefits for both students and lecturers of different fields. One of the areas that could benefit from their adoption is such of mobile robotics, whose recent popularity has resulted in an increasing demand of trained practitioners with a solid theoretical and practical background. Therefore, there is a need of high quality learning materials adapted to modern tools and methodologies. With that in mind, this work explores how the introduction of Jupyter notebooks in undergraduate mobile robotic courses contributes to improve both teaching and learning experiences. For that, we first present a series of (publicly available) educational notebooks encompassing a variety of topics relevant for robotics, with a particular emphasis in the study of mobile robots and commonly used sensors. Those documents have been built from the ground up to take advantage of the Jupyter Notebook framework, bridging the typical gap between theoretical frame and interactive code. We also present a case study describing the notebooks usage in undergraduate courses at University of Málaga, including a discussion on the promising results and findings obtained.
The study conducted by Hilton (2016) focused on open educational resources (OER) by analyzing the findings of 16 studies that investigated (a) the influence of OER on academic learning outcomes at the tertiary context, and (b) students’ and instructors’ perceptions of OER in their teaching and learning contexts. Hilton’s analysis of the findings of these studies indicated two major findings: (1) when students use OER, they obtain the same learning outcomes as with traditional textbooks while saving money; and (2) both students and teachers find OER comparable to traditional learning resources in terms of quality. Several advantages of OER were also revealed. These included low or no cost of OER, perceived ease of reading and access, their ability to provide the same learning outcomes as traditional materials, and students’ and instructors’ positive perceptions. By indicating the role of OER in obtaining the same or similar learning, the study has also suggested that OER be considered useful sources for classes and also a valid replacement for commercial textbooks. However, we also need to consider the context where OER will be used and how OER are designed and used in this context since these two determine whether OER will work and suffice. This article considers the contextual factors and design of OER, and the limitations of Hilton’s work in addition to several ideas and suggestions for further research regarding OER in online and face-to-face instruction.
Learning to program involves acquisition of various skills including problem solving, fundamental design techniques as well as critical thinking. Generally, most of the novice programmers struggle to develop all these important skill. The research community has addressed the problem in many different ways while involving improvisations in curriculum, pedagogical methods, cognitive aspects, supporting tools, and in designing assessments. This research aims to analyze and synthesize the existing literature in the aforementioned areas. Research articles pertaining to the area of Introductory Programming Courses (IPC) have been found using appropriate search queries, while nearly 60 research articles, published in last ten years, have been carefully selected by employing a systematic filtering process. The scope of this work only covers the research conducted for IPC in higher education. Main findings of this study show that “solution proposal” and “evaluation research” have been reported as two main research types adopted by these studies. Moreover, pedagogy, language choice and students’ performance analysis are the most frequently addressed aspects of IPC; whereas, curriculum contents, assessment design, and teaching/learning through tools have appeared as less addressed aspects of IPC. Furthermore, a taxonomy of IPC has been presented based on the studied literature. Lastly, general considerations and future research directions have been presented for the practitioners and researchers in this area.
Peer assessment activities might be one of the few personalized assessment alternatives to the implementation of auto-graded activities at scale in Massive Open Online Course (MOOC) environments. However, teacher's motivation to implement peer assessment activities in their courses might go beyond the most straightforward goal (i.e., assessment), as peer assessment activities also have other side benefits, such as showing evidence and enhancing the critical thinking, comprehension or writing capabilities of students. However, one of the main drawbacks of implementing peer review activities, especially when the scoring is meant to be used as part of the summative assessment, is that it adds a high degree of uncertainty to the grades. Motivated by this issue, this paper analyses the reliability of all the peer assessment activities performed as part of the MOOC platform of the Spanish University for Distance Education (UNED) UNED-COMA. The following study has analyzed 63 peer assessment activities from the different courses in the platform, and includes a total of 27,745 validated tasks and 93,334 peer reviews. Based on the Krippendorff's alpha statistic, which measures the agreement reached between the reviewers, the results obtained clearly point out the low reliability, and therefore, the low validity of this dataset of peer reviews. We did not find that factors such as the topic of the course, number of raters or number of criteria to be evaluated had a significant effect on reliability. We compare our results with other studies, discuss about the potential implications of this low reliability for summative assessment, and provide some recommendations to maximize the benefit of implementing peer activities in online courses.
Quality teaching is directly related with effective and high-quality feedback, and such arguments are well supported by the findings of recent studies. Feedback constitutes a central aspect of learning, yet has been largely neglected in research to date, particularly from the student’s point of view. This gap in practice has been picked up by the Department for Teacher Training of “Lucian Blaga” University of Sibiu. This paper explores students’ perception of feedback. First year students enrolled at the Primary and Preschool Pedagogy program were invited to attend focus groups and individual interviews. A questionnaire was developed from themes identified in the focus groups and was administrated to a large group of undergraduate students in the second part of the study. This paper reports on the focus groups and the themes that emerged from the data. Findings revealed that receiving late and minimal feedback during the semester was a common cause of students’ dissatisfaction. Responses were mixed to the question of whether enough feedback was being provided. They indicated they wanted a more proactive approach from the academic staff, because they recognized the value of feedback as central to improved learning.
Different sources of data about students, ranging from static demographics to dynamic behavior logs, can be harnessed from a variety sources at Higher Education Institutions. Combining these assembles a rich digital footprint for students, which can enable institutions to better understand student behaviour and to better prepare for guiding students towards reaching their academic potential. This paper presents a new research methodology to automatically detect students “at-risk” of failing an assignment in computer programming modules (courses) and to simultaneously support adaptive feedback. By leveraging historical student data, we built predictive models using students’ offline (static) information including student characteristics and demographics, and online (dynamic) resources using programming and behaviour activity logs. Predictions are generated weekly during semester. Overall, the predictive and personalised feedback helped to reduce the gap between the lower and higher-performing students. Furthermore, students praised the prediction and the personalised feedback, conveying strong recommendations for future students to use the system. We also found that students who followed their personalised guidance and recommendations performed better in examinations.
Massive Open Online Courses (MOOCs) is an innovative method in modern education, especially important for autonomous study and the sharing of global excellent education resources. However, it is not easy to implement the teaching process according to the specific characters of students by MOOCs because the number of participants is huge and the teacher cannot identify the characters of students through a face to face interaction. As a new subject combined with different areas, such as economics, sociology, environment, and even engineering, the education of sustainability-related courses requires elaborate consideration of individualized teaching for students from diverse backgrounds and with different learning styles. Although the major MOOC platforms or learning management systems (LMSs) have tried lots of efforts in the design of course system and the contents of the courses for sustainability education, the achievements are still unsatisfied, at least the issue of how to effectively take into account the individual characteristics of participants remains unsolved. A hybrid Neural Network (NN) model is proposed in this paper which integrates a Convolutional Neural Networks (CNN) and with a Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) in an effort to detect individual learning style dynamically. The model was trained by learners’ behavior data and applied to predicting their learning styles. With identified learning style for each learner, the power of MOOC platform can be greatly enhanced by being able to offer the capabilities of recommending specific learning path and the relevant contents individually according to their characters. The efficiency of learning can thus be significantly improved. The proposed model was applied to the online study of sustainability-related course based on a MOOC platform with more than 9,400,000 learners. The results revealed that the learners could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.
The evaluation of a program's compliance with service delivery and features necessary for the attainment of the programs educational objectives, student outcomes and continuous improvement is an important element in program accreditation and continuous improvement process. The study reported in this paper investigated the possible effects of students' feedback on the improvement of adult higher education distance learning service quality in a South African rural-based university. The study interrogated the service provision factors that seemingly helped in improving the delivery of the program. Such information are vital for planning, good governance, policy formulation, monitoring and evaluation, and for decision-making. The study used a quantitative descriptive statistics analysis of data generated ranging from 2013-2014. It comprised of overall student satisfaction as the dependent variable and the explanatory variables were given by program management, facilitation, assessment, learner support, systems, resources, program outcomes and subject matter. Analytical results were obtained from the Mann Whitney Test. The population consisted of students enrolled in the Advanced Certificate in Education (ACE) program by distance mode. The 313 respondents sampled were randomly selected from a total population of 916 students. Data were collected using a semi-structured questionnaire. The results revealed that service qualities linked to effective management, facilitation, academic support and subject matter delivery were the main qualities that the students recommended for the improvement of the program. It is therefore recommended that adult higher education programs must have a documented systematically utilized and effective process involving program service delivery constituencies like assessment, academic support and resources provided, for the periodic review of the program educational objectives to ensure that the program remains consistent with the institutional mission, needs and criteria.
This study investigated the complex relationship among learning styles, gender, perceived satisfaction,
and academic performance across four programming courses supported by an e-learning
platform. A total of 219 undergraduate students from a public Iraqi university who recently experienced
e-learning voluntarily took place in the study. The integrated courses adopted a blended
learning mode and all learners were provided the same learning content and pathway irrespective
of their individual styles. Data were gathered using the Index of Learning Styles (ILS), three
closed-ended questions, and the academic record. Traditional statistics and partial least squares
structural equation modelling (PLS-SEM) were performed to examine the proposed hypotheses.
The findings of this research suggested that, overall, learning style dimensions are uncorrelated
with either academic performance or perceived satisfaction, except for the processing dimension
(active/reflective) that has a significant effect on the latter. Furthermore, gender is unassociated
with any of the proposed model’s constructs. Finally, there is no significant correlation between
academic performance and perceived satisfaction. These results led to the conclusion that even
though Arabic engineering students prefer active, sensing, visual, and sequential learning as do
other engineering students from different backgrounds, they can adapt to a learning context even
if their preferences are not met. The research contributes empirically to the existing debate regarding
the potential implications of learning styles and for the Arabic context in particular, since
respective research remains rare.
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students’ change in programming patterns is only weakly predictive of course performance. We subsequently hone in on 1 single assignment, trying to map students’ learning process and trajectories and automatically identify productive and unproductive (sink) states within these trajectories. Results show that our process-based metric has better predictive power for final exams than the midterm grades. We conclude with recommendations about the use of such methods for assessment, real-time feedback, and course improvement.
Programming is an important basic skill for computer science students. However, most students found it is hard to learn and score a good mark. This paper discusses the study to identify problems and causes faced by programming students. The study was conducted at the Faculty of Information Science and Technology, UKM. Data was collected from 179 students who answered on–line questionnaire. The study identified several major problems; reference materials, lectures and lab session approaches, problem solving ability, time management and self-confidence. The findings will be used to improve the courses and thus ensures the realization of course outcomes, decrease the failure rates and increase student's interest in programming.
Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.
The research on formative assessment and feedback is reinterpreted to show how these processes can help students take control of their own learning, i.e. become self-regulated learners. This refor-mulation is used to identify seven principles of good feedback practice that support self-regulation. A key argument is that students are already assessing their own work and generating their own feedback, and that higher education should build on this ability. The research underpinning each feedback principle is presented, and some examples of easy-to-implement feedback strategies are briefly described. This shift in focus, whereby students are seen as having a proactive rather than a reactive role in generating and using feedback, has profound implications for the way in which teachers organise assessments and support learning.
There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students
One of the most commonly accepted models of relationships among three variables in applied industrial and organizational psychology is the simple moderator effect. However, many authors have expressed concern over the general lack of empirical support for interaction effects reported in the literature. We demonstrate in the current sample that use of a continuous, dependent-response scale instead of a discrete, Likert-type scale, causes moderated regression analysis effect sizes to increase an average of 93%. We suggest that use of relatively coarse Likert scales to measure fine dependent responses causes information loss that, although varying widely across subjects, greatly reduces the probability of detecting true interaction effects. Specific recommendations for alternate research strategies are made.
New developments to computer-aided design (CAD) software transform a once solitary modelling task into a collaborative one. The emerging multi-user CAD (MUCAD) systems allow virtual, real-time collaboration, with the potential to expand the learning outcomes and teaching methods of CAD. This paper proposes a MUCAD collaborative learning framework (MUCAD-CLF) to interpret backend analytic data from commercially available MUCAD software. The framework builds on several existing metrics from the literature and introduces newly developed methods to classify CAD actions collected from users’ analytic data. The framework contains two different classification approaches of user actions, categorizing actions by action type (e.g., creating, revising, viewing) and by design space (e.g., constructive, organizing), for comparative analysis. Next, the analytical framework is applied via a collaborative design challenge, corresponding to over 20,000 actions collected from 31 participants. Illustrative analyses utilizing the MUCAD-CLF are presented to demonstrate the resulting insight. Differences in CAD behaviour, indicating differences in learning, are observed between teams made up entirely of novices, entirely of experienced users, or a mix. In pairs of experts and novices, we see both a perceived high-satisfaction apprenticeship experience for the novices and preliminary evidence of an increase in expert design behaviours for the novices. The proposed framework is critical for MUCAD systems to make the most of the educational possibility of combining technical skill-building with team collaboration. Preliminary evidence collected in a fully-virtual design learning activity, and analyzed using the proposed MUCAD-CLF, shows that novice students gain advanced CAD design knowledge when collaborating with experienced teammates. With the user data captured by modern MUCAD software and the MUCAD-CLF presented herein, instructors and researchers can more efficiently assess and visualize students’ performance over the design learning process.
This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. A data analysis library is also provided to support this process. Alloy4Fun has been used in formal methods graduate courses for 3 years and for the latest editions we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).
In recent years, the government has actively set up computer programming courses to train those with the relevant talent; however, the learning performance of the students is not ideal. Therefore, in order to learn programming skills, students usually adopt note-taking strategies because, due to the pressure of the course, the teachers do not have sufficient time to help the students to fully understand the course content. This means that some students take notes without thinking, so their academic performance is usually poor. This study, therefore, proposes an innovative curriculum design that is based on the “Note-taking System and Teaching Strategy’’ (NSTS), which combines learning style concepts and peer learning concepts to achieve student interaction and promote their thinking skills. In the learning activity, students are asked to search for additional supplementary material and to write their own notes, and then the members of group can read their notes and learn from them. However, the results of the study show that the NSTS curriculum design exhibits significant differences for improving the students' academic performance, and that it also has a certain influence on their learning motivation.
Asynchronous online discussion is one of the widely used collaborative learning activities in digital learning that can facilitate learners to think of controversial problems, share ideas, ask questions, and give feedback without time and space constraints. However, if there is no appropriate and effective mechanism that can promote asynchronous online discussion performance, it may cause the contents of the discussion away from the discussion topic, too narrow, or not deep enough. Therefore, this research presents a Topic Analysis Instant Feedback System (TAIFS) that can explore and visualize the topics according to the contents of the asynchronous online discussion from the learners of different groups. The TAIFS was developed and integrated into Modular Object-Oriented Dynamic Learning Environment (Moodle) which is a free and open source digital learning platform that has been widely used around the world based on Latent Dirichlet Allocation (LDA) to help learners grasp the change of overall discussion topics in real-time and see the similarities and differences of the discussion among different groups, thereby effectively improving their asynchronous online discussion effectiveness. A quasi-experimental research method was adopted to examine the effects of the proposed TAIFS-assisted online discussion on learners’ socio-scientific reasoning performance including the complexity and multiple perspectives. A total of 61 students from two classes in a high school in Taipei city, Taiwan were recruited as the research participants. One class of 31 students was assigned to the experimental group using the TAIFS-assisted discussion board for discussing the socio-scientific issue (SSI) entitled “Utilization of the Coastal Area,” while the remaining 30 students from another class were assigned to the control group using a general Moodle discussion board. Analytical results show that the experimental group was significantly better than the control group in the overall discussion performance and the complexity and multiple perspectives. Besides, TAIFS is more helpful to improve the overall discussion performance and multiple perspectives of female learners than male learners. The contribution of this study is to propose an innovative and effective TAIFS that can promote learners’ asynchronous online discussion effectiveness to bring the asynchronous online discussion into new ground.
This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. Alloy4Fun has been used in formal methods graduate courses for two years and for the latest edition we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).
Design thinking is often hidden and implicit, so empirical approach based on experiments and data-driven methods has been the primary way of doing such research. In support of empirical studies, design behavioral data which reflects design thinking becomes crucial, especially with the recent advances in data mining and machine learning techniques. In this paper, a research platform that supports data-driven design thinking studies is introduced based on a computer-aided design (cad) software for solar energy systems, energy3d, developed by the team. We demonstrate several key features of energy3d including a fine-grained design process logger, embedded design experiment and tutorials, and interactive cad interfaces and dashboard. These features make energy3d a capable testbed for a variety of research related to engineering design thinking and design theory, such as search strategies, design decision-making, artificial intelligent (AI) in design, and design cognition. Using a case study on an energy-plus home design challenge, we demonstrate how such a platform enables a complete research cycle of studying designers” sequential decision-making behaviors based on fine-grained design action data and unsupervised clustering methods. The results validate the utility of energy3d as a research platform and testbed in supporting future design thinking studies and provide domain-specific insights into new ways of integrating clustering methods and design process models (e.g., the function–behavior–structure model) for automatically clustering sequential design behaviors.
Purpose
An ideal learning analytics tool for programming exercises performs the role of a lecturer who monitors the code development, provides customized support and identifies students at risk to drop out. But a reliable prediction and prevention of drop-out is difficult, due to the huge problem space in programming tasks and variety of solutions and programming strategies. The purpose of this paper is to tackle this problem by, first, identifying activity patterns that indicate students at risk; and, second, finding reasons behind specific activity pattern, for identification of instructional interventions that prevent drop-out.
Design/methodology/approach
The authors combine two investigation strategies: first, learning analytic techniques (decision trees) are applied on features gathered from students, while completing programming exercises, in order to classify predictors for drop-outs. Second, the authors determine cognitive, motivational and demographic learner characteristics based on a questionnaire. Finally, both parts are related with a correlation analysis.
Findings
It was possible to identify generic variables that could predict early and later drop-outs. For students who drop out early, the most relevant variable is the delay time between availability of the assignment and the first login. The correlation analysis indicates a relation with prior programming experience in years and job occupation per week. For students who drop out later in the course, the number of errors within the first assignment is the most relevant predictor, which correlates with prior programming skills.
Originality/value
The findings indicate a relation between activity patterns and learner characteristics. Based on the results, the authors deduce instructional interventions to support students and to prevent drop-outs.
Learning behavioral engagement is an important learning feature that affects students' learning achievement and persistence. As the main driving force of students’ actual performance, there is not effective evaluation model of learning behavioral engagement for MOOCs platform. It is helpful to observe the change rule of students’ participation and activity by analyzing the data of learning behavioral engagement. In this paper, the online study data and feedback learning behavioral engagement data from four MOOCs course were analyzed, and the predictive effect of behavior variables on learning behavioral engagement were studied. According to the results of data analysis, persistence, reflection, initiative and concentration are identified as the assessment dimensions of level which can represent learning behavioral engagement. Based on this, the evaluation model based on MOOCs platform is constructed. The establishment of the model can lay the foundation for the design of related evaluation and analysis tools.
The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.
Conducting experiments is an important practice for both engineering design and scientific inquiry. Engineers iteratively conduct experiments to evaluate ideas, make informed decisions, and optimize their designs. However, both engineering design and scientific experimentation are open‐ended tasks that are not easy to assess. Recent studies have demonstrated how technology‐based assessments can help to capture and characterize these open‐ended tasks using unobtrusive data logging. This study builds upon a model to characterize students' experimentation strategies in design (ESD). Ten undergraduate students worked on a design challenge using a computer‐aided design (CAD) tool that captured all their interactions with the software. This “process data” was compared to “think‐aloud data,” which included students' explanations of their rationale for the experiments. The results suggest that the process data and the think‐aloud data have both affordances and limitations toward the goal of assessing students' ESD. While the process data was an effective approach to identify relevant sequences of actions, this type of data failed to ensure that students carried them out with a specific purpose. On the other hand, the think‐aloud data captured students' rationale for conducting experiments, but it depended on students' ability to verbalize their actions. In addition, the implementation of think‐aloud procedures and their analysis are time consuming tasks, and can only be done individually.
Rationale and objectives:
The goal of this study is to demonstrate how the teaching of radiology physics can be enhanced with the use of interactive scientific notebook software.
Methods:
We used the scientific notebook software known as Project Jupyter, which is free, open-source, and available for the Macintosh, Windows, and Linux operating systems.
Results:
We have created a scientific notebook that demonstrates multiple interactive teaching modules we have written for our residents using the Jupyter notebook system.
Conclusions:
Scientific notebook software allows educators to create teaching modules in a form that combines text, graphics, images, data, interactive calculations, and image analysis within a single document. These notebooks can be used to build interactive teaching modules, which can help explain complex topics in imaging physics to residents.
Objectives:
The goal of this article is to introduce the concept of scientific notebook software, and to illustrate how it can be used to document a research project, to perform image analysis and to create interactive teaching tools.
Methods:
We describe the installation of scientific notebook software known as Project Jupyter, which is free, open-source and available for the Macintosh, Windows and Linux operating systems.
Results:
We have created 2 scientific notebooks that demonstrate applications germane to radiologists, particularly those in academic radiology. The first notebook provides a tutorial that summarizes basic features of the Project Jupyter notebook, and gives numerous examples of how the notebooks can display explicatory text, perform statistical computations, and display plots, interactive graphics, and audio files. The second notebook provides a toolbox for viewing and manipulating images in the Digital Imaging and Communications in Medicine format.
Conclusions:
Scientific notebook software allows its users to document their work in a form that combines text, graphics, images, data, interactive calculations and image analysis within a single document. Scientific notebooks also provide interactive teaching tools, which are can help explain complex topics in imaging physics to residents.
This article reports on an action research project on improving a functional programming course by moving toward a practical and flexible study environment-flipped and blended classroom. Teaching the topic of functional programming was found to be troublesome using a traditional lectured course format. The need to increase students' amount of practice emerged while subsequent challenges relating to students' independent practical coursework were observed. Particular concerns relating to group work, learning materials, and the attribute of flexibility were investigated during the third action research cycle. The research cycle was analyzed using a qualitative survey on students' views, teacher narrative, and students' study activity data. By this third research cycle, we found that (i) the "call for explanation" is an apt conceptualization for supporting independent work, and in particular for the design of learning materials; (ii) use of studentselected groups that can be flexibly resized or even disbanded enables spontaneous peer support and can avoid frustration about group work; and (iii) students greatly appreciate the high degree of flexibility in the course arrangements but find that it causes them to slip from their goals. The project has improved our understanding of a successful implementation of the target course based on group work and learning materials in the context of independent study, while the attribute of flexibility revealed a contradiction that indicates the need for further action.
Basic
Programming is a mandatory course that covers the fundamentals of programming in Computer Engineering degrees. During the last years, the authors have experimented different approaches to improve the course. For example, they have included Lego Mindstorms robots and visual programming environments in their lectures. However, the heterogeneity of the students in the course significantly affects the course development. To overcome this problem, the next step entails the adoption of adaptive learning systems in the frame of Blended Learning (B-Learning). In this context, the OWLish generic architecture has been defined. This paper centers on the adaptation of the Domain Model of OWLish to meet the requirements of programming courses.
The chief executive officer of edX, Anant Agarwal, declared that Massive Open Online Courses (MOOCs) should serve as “particle accelerator for learning” ( 1 ). MOOCs provide new sources of data and opportunities for large-scale experiments that can advance the science of learning. In the years since MOOCs first attracted widespread attention, new lines of research have begun, but findings from these efforts have had few implications for teaching and learning. Big data sets do not, by virtue of their size, inherently possess answers to interesting questions. For MOOC research to advance the science of learning, researchers, course developers, and other stakeholders must advance the field along three trajectories: from studies of engagement to research about learning, from investigations of individual courses to comparisons across contexts, and from a reliance on post hoc analyses to greater use of multidisciplinary, experimental design.
It is not uncommon that researchers face difficulties when performing meaningful cross-study comparisons for research. Research associated with the distance learning realm can be even more difficult to use as there are different environments with a variety of characteristics. We implemented a mixed-method analysis of research articles to find out how they define the learning environment. In addition, we surveyed 43 persons and discovered that there was inconsistent use of terminology for different types of delivery modes. The results reveal that there are different expectations and perceptions of learning environment labels: distance learning, e-Learning, and online learning.
Hall Giesinger andAnanthanarayanan NMC Horizon Report 2017 Higher Education Edition. The New Media Consortium
- Adams Becker
- M Cummins
- A Davis
- A Freeman