The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in class teaching is becoming less popular with the young generation, the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses is higher than that of more traditional ones, and the reduced in person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML) based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML based techniques requires a large amount of data seems to be a bottleneck when dealing with small scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students overall performance but also that it could be used to propose timely intervention strategies to boost the students performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas Tech University（德克萨斯理工大学）
This paper investigates how high school students approach computing through an introductory computer science course situated in the Logic Programming (LP) paradigm. This study shows how novice students operate within the LP paradigm while engaging in foundational computing concepts and skills, and presents a case for LP as a viable paradigm choice for introductory CS courses.
Current Educational system uses grades or marks to assess the performance of the student. The marks or grades a students scores depends on different parameters, the main parameter being the difficulty level of a course. Computation of this difficulty level may serve as a support for both the students and teachers to fix the level of training needed for successful completion of course. In this paper, we proposed a methodology that estimates the difficulty level of a course by mapping the Bloom's Taxonomy action words along with Accreditation Board for Engineering and Technology (ABET) criteria …
Massive Open Online Courses (MOOC) are seen as a next step in distance online learning. In the MOOC vision, large numbers of students can access the course content over the Internet and complete courses at their own pace while interacting with their peers and instructors online. Despite the initial enthusiasm about MOOCs, large number of students were observed dropping out of the online courses. In this paper, we pinpoint the reasons behind the high student dropout rate and discuss how the interaction capabilities of MOOCs contributed towards the low completion rate.
The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future-course grade predictions methods based on sparse linear models and low-rank matrix factorizations that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical…
This paper provides an overview of students behavior analysis on a learning management system (LMS), Blackboard (Bb) Learn for a core data communications course of the Undergraduate IT program in the Information Sciences and Technology (IST) Department at George Mason University (GMU). This study is an attempt to understand the navigational behavior of students on Blackboard Learn which can be further attributed to the overall performance of the students. In total, 160 undergraduate students participated in the study. Vast amount of students activities data across all four sections of the cour…
Online tools provide unique access to research students' study habits and problem-solving behavior. In MOOCs, this online data can be used to inform instructors and to provide automatic guidance to students. However, these techniques may not apply in blended courses with face to face and online components. We report on a study of integrated user-system interaction logs from 3 computer science courses using four online systems: LMS, forum, version control, and homework system. Our results show that students rarely work across platforms in a single session, and that final class performance can b…
This article is about the author's experience with developing and teaching an introductory electrical engineering course for students of Faculty (department) of Information Technology of a Russian university. The curriculum of this department conforms to typical computer science curricula of US engineering schools with a noticeable omission of comparable electrical engineering courses. When developing the course, I did my best to pay attention to learning preferences of the department's student body. I also hoped to contribute to a degree to meeting labor market demands for developers of elect…
University of Miami（迈阿密大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multi-armed bandits, an…
Students' satisfaction plays a vital role in success of an educational institute. Hence, many educational institutes continuously improve their service to produce a supportive learning environment to satisfy the student need. For this reason, educational institutions collect student satisfaction data to make decision about institutional quality, but till now it cannot be determined because student satisfaction is a complex matter which is influenced by variety of characteristics of students and institutions. There are many studies have been performed to inspect student satisfaction in the form…
This Full Paper in the Research-To-Practice Category presents a long-term study about the effects of a student-centered course on communication and professional skills on students' thoughts, attitudes, and behavior. The course is offered at a European university as part of a computer science master's program. This paper shares the design and challenges of a longitudinal study that reaches ten years behind and employs a mixed-methods approach. Besides presenting and interpreting the findings, we shed light on which features tend to stay on students' minds and impact their way of being and actin…
Massive Open Online Courses (MOOCs) once offered the promise of accessibility and affordability. However, MOOCs typically lack expert feedback and social interaction, and have low student engagement and retention. Thus, alternative programs for online education have emerged including an online graduate program in computer science at a major public university in USA. This program is considered a success with over 9000 students now enrolled in the program. We adopt the perspective of cognitive science to answer the question why do only some online educational courses succeed? We measure learner …
Contribution: This study examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy shed a light into understanding academic success, which must be considered when developing effective learning strategies for programming. Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy related to each other and student success in programming? Is it possible to predict student course performance using a subset of these constructs…
We analyze learning data of an e-assessment platform for an introductory mathematical statistics course, more specifically the time of the day when students learn. We propose statistical models to predict students' success and to describe their behavior with a special focus on the following aspects. First, we find that learning during daytime and not at nighttime is a relevant variable for predicting success in final exams. Second, we observe that good and very good students tend to learn in the afternoon, while some students who failed our course were more likely to study at night but not suc…
Distance learning universities usually afford their students the flexibility to advance their studies at their own pace. This can lead to a considerable fluctuation of student populations within a program's courses, possibly affecting the academic viability of a program as well as the related required resources. Providing a method that estimates this population could be of substantial help to university management and academic personnel. We describe how to use course precedence constraints to calculate alternative tuition paths and then use Markov models to estimate future populations. In doin…
This study attempted to develop fair, relevant, and content-valid assessment tools for capstone project courses. Toward this goal, new rating instruments based on the concept of rubrics were proposed. To ensure that the new instruments were valid and fair, several meetings with faculty and students of the computing science departments (i.e., Computer Science and Information Technology) were successively conducted. Eight faculty members and 10 students participated in the study. The final versions of the instruments were completed after a series of careful deliberations with faculty and student…
The identification and analysis of student satisfaction is a challenging issue. This is becoming increasingly important since a measure of student satisfaction is taken as an indication of how well a course has been taught. However, it remains a challenging problem as student satisfaction has various aspects. In this paper, we formulate the student satisfaction estimation as a prediction problem where we predict different levels of student satisfaction and infer the influential predictors related to course and instructor. We present five different aspects of student satisfaction in terms of 1)…
Paris-Saclay University（巴黎-萨克雷大学）；Instituto Nacional de Astrofísica, Óptica y Electrónica（国家天体物理、光学与电子学研究所）
Data-driven decision making is serving and transforming education. We approached the problem of predicting students' performance by using multiple data sources which came from online courses, including one we created. Experimental results show preliminary conclusions towards which data are to be considered for the task.
Growth mindset interventions foster students' beliefs that their abilities can grow through effort and appropriate strategies. However, not every student benefits from such interventions - yet research identifying which student factors support growth mindset interventions is sparse. In this study, we utilized machine learning methods to predict growth mindset effectiveness in a nationwide experiment in the U.S. with over 10,000 students. These methods enable analysis of arbitrarily-complex interactions between combinations of student-level predictor variables and intervention outcome, defined …
In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses. In this paper, we use Collaborative Filtering (CF), Matrix Factorization (MF), and Restricted Boltzma…
Millions of learners worldwide are now using intelligent tutoring systems (ITSs). At their core, ITSs rely on machine learning algorithms to track each user's changing performance level over time to provide personalized instruction. Crucially, student performance models are trained using interaction sequence data of previous learners to analyse data generated by future learners. This induces a cold-start problem when a new course is introduced for which no training data is available. Here, we consider transfer learning techniques as a way to provide accurate performance predictions for new cou…
Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our dataset included 378,000 users and 1,000,000 unique registration events in France Universite Numerique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion rates with a Weibull survival function, following the intuition that students "survive" in a course fo…
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Fine-grained clickstream data is collected through Diderot, a non-commercial educationa…
Blended courses have become the norm in post-secondary education. Universities use large-scale learning management systems to manage class content. Instructors deliver readings, lectures, and office hours online; students use intelligent tutors, web forums, and online submission systems; and classes communicate via web forums. These online tools allow students to form new social networks or bring social relationships online. They also allow us to collect data on students' social relationships. In this paper we report on our research on community formation in blended courses based on online for…
One of the leading university goals is to provide the students with the necessary skills for better functioning in their future studies. Gaining and developing skills, both technical and soft skills, are the critical building blocks for a successful career. The traditional teaching process, which includes delivering lectures and conducting exams, emphasizes the upliftment of technical knowledge rather than building self-esteem and enhancing skills development among students. Development of non-technical skills like self-esteem, life-long learning among students is vital for a successful career…
Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
Course load analytics (CLA) inferred from LMS and enrollment features can offer a more accurate representation of course workload to students than credit hours and potentially aid in their course selection decisions. In this study, we produce and evaluate the first machine-learned predictions of student course load ratings and generalize our model to the full 10,000 course catalog of a large public university. We then retrospectively analyze longitudinal differences in the semester load of student course selections throughout their degree. CLA by semester shows that a student's first semester …
Students' interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict students' performance in classes. One key feature of these actions that is often overlooked is how and when the students transition between different online platforms. In this work, we study sequences of student transitions between online tools in blended…
Online courses, as a pedagogical approach to teaching, boomed during this Coronavirus Disease 2019 pandemic era. Universities shifted from traditional face to face classes to online distance learning due to the cause of the pandemic. This study aimed to determine how effective an online course is in learning a programming course. The study utilized mixed method research applied through a validated survey questionnaire consisting of closed and open ended questions. Python programming was the course selected to undergo the study and underwent an evaluation to determine the students' responses. S…
University of California, Berkeley（加州大学伯克利分校）；UC Berkeley School of Information（加州大学伯克利分校信息学院）
This paper presents several strategies that can improve neural network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM networks enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.
The increasingly fast development cycle for online course contents, along with the diverse student demographics in each online classroom, make real-time student outcomes prediction an interesting topic for both industrial research and practical needs. In this paper, we tackle the problem of real-time student performance prediction in an on-going course using a domain adaptation framework. This framework is a system trained on labeled student outcome data from previous coursework but is meant to be deployed on another course. In particular, we introduce a GritNet architecture, and develop an un…
Increasingly fast development and update cycle of online course contents, and diverse demographics of students in each online classroom, make student performance prediction in real-time (before the course finishes) and/or on curriculum without specific historical performance data available interesting topics for both industrial research and practical needs. In this research, we tackle the problem of real-time student performance prediction with on-going courses in a domain adaptation framework, which is a system trained on students' labeled outcome from one set of previous coursework but is me…
University of Califonia, San Diego 9500 Gilman Dr San Diego California 92093-0404；University of Califonia, San Diego
We describe our experience teaching an intensive capstone course in which pairs of students build the hardware and software for a remote-controller quad-rotor aircraft (i.e., a quadcopter or "drone") from scratch in one 10-week quarter. The course covers printed circuit board (PCB) design and assembly, basic control theory and sensor fusion, and embedded systems programming. To reduce the workload on course staff and provide higher-quality feedback on student designs, we have implemented an automated PCB design checking tool/autograder. We describe the course content in detail, identify the ch…
University course ranking forums are a popular means of disseminating information about satisfaction with the quality of course content and instruction, especially with undergraduate students. A variety of policy decisions by university administrators, instructional designers and teaching staff affect how students perceive the efficacy of pedagogies employed in a given course, in class and online. While there is a large body of research on qualitative driving factors behind the use of academic rating sites, there is little investigation of the (potential) implicit student bias on said forums t…
The large-scale online management systems (e.g. Moodle), online web forums (e.g. Piazza), and online homework systems (e.g. WebAssign) have been widely used in the blended courses recently. Instructors can use these systems to deliver class content and materials. Students can communicate with the classmates, share the course materials, and discuss the course questions via the online forums. With the increased use of the online systems, a large amount of students' interaction data has been collected. This data can be used to analyze students' learning behaviors and predict students' learning ou…
University students routinely use the tools provided by online course ranking forums to share and discuss their satisfaction with the quality of instruction and content in a wide variety of courses. Student perception of the efficacy of pedagogies employed in a course is a reflection of a multitude of decisions by professors, instructional designers and university administrators. This complexity has motivated a large body of research on the utility, reliability, and behavioral correlates of course rankings. There is, however, little investigation of the (potential) implicit student bias on the…
Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experienc…
Department of Computer Science and Engineering University of Minnesota（明尼苏达大学计算机科学与工程系）
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, prior courses taken by a student can have \black{…
To enhance student learning, we demonstrate an experimental study to analyze student learning outcomes in online and in-class sections of a core data communications course of the Undergraduate IT program in the Information Sciences and Technology (IST) Department at George Mason University (GMU). In this study, student performance is evaluated based on course assessments. This includes home and lab assignments, skill-based assessment, and traditional midterm exam across all 4 sections of the course. All sections have analogous content, assessment plan and teaching methodologies. Student demogr…
Federal University, Oye-Ekiti（奥耶埃基蒂联邦大学）；Federal University of Technology, Akure（阿库雷联邦理工大学）；Caleb University（迦勒大学）；LAUTECH（拉多克·阿金托拉科技大学）
Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students per…
