Daniel T. Seaton

Who does what in a massive open online course

Student-participation data from the inaugural MITx (now edX) course---6.002x: Circuits and Electronics---unpacks MOOC student behavior.

Understanding in-video dropouts and interaction peaks inonline lecture videos

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

Microcanonical entropy inflection points: key to systematic understanding of transitions in finite systems.

We introduce a systematic classification method for the analogs of phase transitions in finite systems. This completely general analysis, which is applicable to any physical system and extends toward the thermodynamic limit, is based on the microcanonical entropy and its energetic derivative, the inverse caloric temperature. Inflection points of this quantity signal cooperative activity and thus serve as distinct indicators of transitions. We demonstrate the power of this method through application to the long-standing problem of liquid-solid transitions in elastic, flexible homopolymers.

Privacy, anonymity, and big data in the social sciences

Open data has tremendous potential for science, but, in human subjects research, there is a tension between privacy and releasing high-quality open data. Federal law governing student privacy and the release of student records suggests that anonymizing student data protects student privacy. Guided by this standard, we de-identified and released a data set from 16 MOOCs (massive open online courses) from MITx and HarvardX on the edX platform. In this article, we show that these and other de-identification procedures necessitate changes to data sets that threaten replication and extension of baseline analyses. To balance student privacy and the benefits of open data, we suggest focusing on protecting privacy without anonymizing data by instead expanding policies that compel researchers to uphold the privacy of the subjects in open data sets. If we want to have high-quality social science research and also protect the privacy of human subjects, we must eventually have trust in researchers. Otherwise, we’ll always have the strict tradeoff between anonymity and science illustrated here.

Correlating skill and improvement in 2 MOOCs with a student's time on tasks

Because MOOCs offer complete logs of student activities for each student there is hope that it may be possible to find out which activities are the most useful for learning. We start this quest by examining correlations between time spent on specific course resources and various measures of student performance: score on assessments, skill as defined by Item Response Theory, improvement in skill over the period of the course, and conceptual improvement as measured by a pre-post test. We study two MOOCs offered on edX.org by MIT faculty: Circuits and Electronics (6.002x) and Mechanics Review (8.MReV). Surprisingly, we find strong negative correlations in 6.002x between student skill and resource use; we attribute these findings to the fact that students with higher initial skills can do the exercises faster and with less time spent on instructional resources. We find weak or slightly negative correlations between relative improvement and resource use in 6.002x. The correlations with learning are stronger for conceptual knowledge in 8.MReV than with relative improvement, but similar for all course activities (except that eText checkpoint questions correlate more strongly with relative improvement). Clearly, the wide distribution of demographics and initial skill in MOOCs challenges us to isolate the habits of learning and resource use that correlate with learning for different students.

Due dates in MOOCs: does stricter mean better?

Massive Open Online Courses (MOOCs) employ a variety of components to engage students in learning (eg. videos, forums, quizzes). Some components are graded, which means that they play a key role in a students final grade and certificate attainment. It is not yet clear how the due date structure of graded components affects student outcomes including academic performance and alternative modes of learning of students. Using data from HarvardX and MITx, Harvards and MITs divisions for online learning, we study the structure of due dates on graded components for 10 completed MOOCs. We find that stricter due dates are associated with higher certificate attainment rates but fewer students who join late being able to earn a certificate. Our findings motivate further studies of how the use of graded components and deadlines affects academic and alternative learning of MOOC students, and can help inform the design of online courses.

Analyzing the impact of course structure on electronic textbook use in blended introductory physics courses

We investigate how elements of course structure (i.e., the frequency of assessments as well as the sequencing and weight of course resources) influence the usage patterns of electronic textbooks (e-texts) in introductory physics courses. Specifically, we analyze the access logs of courses at Michigan State University and the Massachusetts Institute of Technology, each of which deploy e-texts as primary or secondary texts in combination with different formative assessments (e.g., embedded reading questions) and different summative assessment (exam) schedules. As such studies are frequently marred by arguments over what constitutes a “meaningful” interaction with a particular page (usually judged by how long the page remains on the screen), we consider a set of different definitions of “meaningful” interactions. We find that course structure has a strong influence on how much of the e-texts students actually read, and when they do so. In particular, courses that deviate strongly from traditional structures,...

Effects Of Stiffness On Short, Semiflexible Homopolymer Chains

Conformational and transition behavior of finite, semiflexible homopolymers is studied using an extension of the Wang–Landau algorithm. Generation of a flat distribution in the sampling parameters energy and stiffness allows for efficient investigation of transitions between various conformational phases. Of particular importance is the ability to predict behavior for a given stiffness value, where three classes of minimum energy conformations are expected: Solid-globular, rod-like and toroidal. We present first results highlighting the behavior of a single N = 20 length chain.

Exploring physics students’ engagement with online instructional videos in an introductory mechanics course

The advent of new educational technologies has stimulated interest in using online videos to deliver content in university courses. We examined student engagement with 78 online videos that we created and were incorporated into a one-semester flipped introductory mechanics course at the Georgia Institute of Technology. We found that students were more engaged with videos that supported laboratory activities than with videos that presented lecture content. In particular, the percentage of students accessing laboratory videos was consistently greater than 80% throughout the semester. On the other hand, the percentage of students accessing lecture videos dropped to less than 40% by the end of the term. Moreover, the fraction of students accessing the entirety of a video decreases when videos become longer in length, and this trend is more prominent for the lecture videos than the laboratory videos. The results suggest that students may access videos based on perceived value: students appear to consider the laboratory videos as essential for successfully completing the laboratories while they appear to consider the lecture videos as something more akin to supplemental material. In this study, we also found that there was little correlation between student engagement with the videos and their incoming background. There was also little correlation found between student engagement with the videos and their performance in the course. An examination of the in-video content suggests that students engaged more with concrete information that is explicitly required for assignment completion (e.g., actions required to complete laboratory work, or formulas or mathematical expressions needed to solve particular problems) and less with content that is considered more conceptual in nature. It was also found that students’ in-video accesses usually increased toward the embedded interaction points. However, students did not necessarily access the follow-up discussion of these interaction points. The results of the study suggest ways in which instructors may revise courses to better support student learning. For example, external intervention that helps students see the value of accessing videos may be required in order for this resource to be put to more effective use. In addition, students may benefit more from a clicker question that reiterates important concepts within the question itself, rather than a clicker question that leaves some important concepts to be addressed only in the discussion afterwards.

Multidimensional student skills with collaborative filtering

Despite the fact that a physics course typically culminates in one final grade for the student, many instructors and researchers believe that there are multiple skills that students acquire to achieve mastery. Assessment validation and data analysis in general may thus benefit from extension to multidimensional ability. This paper introduces an approach for model determination and dimensionality analysis using collaborative filtering (CF), which is related to factor analysis and item response theory (IRT). Model selection is guided by machine learning perspectives, seeking to maximize the accuracy in predicting which students will answer which items correctly. We apply the CF to response data for the Mechanics Baseline Test and combine the results with prior analysis using unidimensional IRT.