Time to Completion of Web‐based Physics Problems With Tutoring

Warnakulasooriya, Rasil; Palazzo, David J.; Pritchard, David E.

doi:10.1901/jeab.2007.70-06

Cited by 17 publications

(14 citation statements)

References 20 publications

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“…These studies have often relied on grades given by classroom instructors, percentage of problems answered correctly [36,37], or time spent solving a problem [38]. Although convenient and easy to collect with large numbers of students, these measures can give an inadequate description of a student's problem solving, since none of them directly assesses the characteristics that are known to distinguish experts from novices and the correlation between such indirect measures and problem-solving expertise is unknown.…”

Section: B Research On Assessing Problem Solvingmentioning

confidence: 99%

Assessing student written problem solutions: A problem-solving rubric with application to introductory physics

Docktor

Dornfeld

Frodermann

et al. 2016

Phys. Rev. Phys. Educ. Res.

126

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Problem solving is a complex process valuable in everyday life and crucial for learning in the STEM fields. To support the development of problem-solving skills it is important for researchers and curriculum developers to have practical tools that can measure the difference between novice and expert problem-solving performance in authentic classroom work. It is also useful if such tools can be employed by instructors to guide their pedagogy. We describe the design, development, and testing of a simple rubric to assess written solutions to problems given in undergraduate introductory physics courses. In particular, we present evidence for the validity, reliability, and utility of the instrument. The rubric identifies five general problem-solving processes and defines the criteria to attain a score in each: organizing problem information into a Useful Description, selecting appropriate principles (Physics Approach), applying those principles to the specific conditions in the problem (Specific Application of Physics), using Mathematical Procedures appropriately, and displaying evidence of an organized reasoning pattern (Logical Progression).

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Section: B Research On Assessing Problem Solvingmentioning

confidence: 99%

Assessing student written problem solutions: A problem-solving rubric with application to introductory physics

Docktor

Dornfeld

Frodermann

et al. 2016

Phys. Rev. Phys. Educ. Res.

126

View full text Add to dashboard Cite

show abstract

“…We thus studied the approximately 100GB of time-stamped log data describing student interactions with the inaugural MITx course 6.002x Circuits and Electronics in spring 2012, data at least two orders of magnitude larger than was analyzed in previous studies of online learning. 10,21 We develop and exhibit several ways to study student interactions with course resources. We do not analyze demographic factors, but rather differentiate students by number of assessment items attempted and total time spent in the course.…”

Section: Who Does What In a Massive Open Online Course?mentioning

confidence: 99%

“…There are clearly alternatives to this approach (such as considering all time between opening and answering a problem as problem-solving time 21 ). Our time-accumulation algorithm is partially thwarted by users who open multiple browser windows or tabs; edX developers are considering ways to account for this in the future.…”

Section: 002x Procedures Data Analysismentioning

confidence: 99%

“…In previous studies of online problem solving this information was simply missing. 21 Figure 6 highlights student transitions from problems (while solving them) to other course components, treating homework sets, the midterm, and the final exam as separate assessment types of interest. Figure 6 shows the discussion forum is the most frequent destination during homework problem solving, though lecture videos consume the most time.…”

Section: Total Time (Hrs)/n (Certificate Earners)mentioning

confidence: 99%

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Who does what in a massive open online course?

et al. 2014

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massive open online courses (MOOCs) collect valuable data on student learning behavior; essentially complete records of all student interactions in a selfcontained learning environment, with the benefit of large sample sizes. Here, we offer an overview of how the 108,000 participants behaved in 6.002x -Circuits and Electronics, the first course in MITx (now edX) in the Spring 2012 semester. We divided participants into tranches based on the extent of their assessment activities, ranging from browsers (constituting ~76% of the participants but only 8% of the total time spent in the course) to certificate earners (7% of participants who accounted for 60% of total time). We examined how the certificate earners allocated their time among the various course components and what fraction of each they accessed. We analyze transitions between course components, showing how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how various course components, and transitions among them, influence learning in MOOCs.Though free online courses are not new, 8 they have reached an unprecedented scale since late 2011. Three organizations-Coursera, edX, and Udacity-have released MOOCs 13 drawing more than 100,000 registrants per course. Numbers from these three initiatives have since grown to more than 100 courses and three million total registrants, resulting in 2012 being dubbed "The Year of the MOOC" by the New York Times. 16 Though there has been much speculation regarding how these initiatives may reshape higher education, 6,12,20 little analysis has been published to date describing student behavior or learning in them.Our main objective here is to show how the huge amount of data available in MOOCs offers a unique research opportunity, a means to study detailed student behavior in a self-contained learning environment throughout an Who Does What in a massive open online course?Data collected in moocs provides insight into student behavior, from weekly e-textbook reading habits to contextdependent use of learning resources when solving problems. in 6.002x, 76% of participants were browsers who collectively accounted for only 8% of time spent in the course, whereas, the 7% of certificate-earning participants averaged 100 hours each and collectively accounted for 60% of total time.Students spent the most time per week interacting with lecture videos and homework, followed by discussion forums and online laboratories; however, interactions with the videos and lecture questions were distinctly bimodal, with half the certificate earners accessing less than half of these resources.illuStration by anthony freda

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“…Usage statistics include simple measures such as the total number of visits or number of visits per page (Pahl and Donnellan, 2003), or the participation of learners in multiple learning sessions over time (Zorrilla et al, 2005). Quantification of usage might focus on time spent solving individual problems (Nilakant and Mitrovic, 2005;Warnakulasooriya, Palazzo, and Pritchard, 2007). In Computer aided Language Learning, tracking data have been used for various purposes, including completion of assignments (Opp-Beckman and Kieffer, 2004), assessment of learning (Goodfellow, 1999), identification of problem areas for follow-up learning activities (Colpaert, 2004), determination of the optimum number of participants in computer-mediated communication (CMC) sessions (Böhlke, 2003), examination of students' degree of participation in CMC sessions (Chun, 1994), and how students interact with specific software components (Chapelle, 2001(Chapelle, , 2003.…”

Section: A Background and Rationalementioning

confidence: 99%

Study of Usage Patterns and Learning Gains in a Web‐based Interactive Static Course

Steif

Dollár

2009

J of Engineering Edu

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Background Courseware for engineering education can feature many discrete interactive learning elements, and typically student usage is not compelled. To take advantage of such courseware, self‐regulation of learning may be necessary. Evaluation of courseware should consider actual usage, learning gains, and indications of learning self‐regulation. Purpose (Hypothesis) The research question focuses on how students' interactions with the courseware affect their learning gains. The hypothesis tested is that learning gains from online courseware increase with usage, and particularly with usage that suggests learning self‐regulation. Design/Method Students in a lecture‐based statics course were assigned to study previously developed courseware as part of homework assignments. Learning gains were deduced from pre‐ and post‐ paper and pencil diagnostic quizzes, and from the first class exam. Credit was based on quiz scores, rather than courseware usage. Usage of interactive elements of the courseware was inferred from log files of students' interactions with the courseware, and patterns suggesting learning self‐regulation were identified. Results High, statistically significant learning gains were found. Substantial usage was evident, with core learning activities initiated by, on average, three‐quarters of students. Learning gains and performance on the relevant class exam appeared to be more closely correlated with usage that indicated self‐regulation of learning rather than with total usage of the courseware. Conclusions Methods of assessing courseware should go beyond courseware features, learning gains, and student self‐reports of effectiveness to include monitoring of actual usage and analyses relating usage to learning. Self‐regulation of learning is likely to be critical to successful usage of courseware, and courseware should be designed to encourage it.

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Time to Completion of Web‐based Physics Problems With Tutoring

Cited by 17 publications

References 20 publications

Assessing student written problem solutions: A problem-solving rubric with application to introductory physics

Assessing student written problem solutions: A problem-solving rubric with application to introductory physics

Who does what in a massive open online course?

Study of Usage Patterns and Learning Gains in a Web‐based Interactive Static Course

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