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Data-driven Learner Modeling to Understand and Improve Online Learning
MOOCs and technology to advance learning and learning research (Ubiquity symposium)

Ubiquity, Volume 2014 Issue May, May 2014 | BY Kenneth R. Koedinger , Elizabeth A. McLaughlin , John C. Stamper 

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Ubiquity

Volume 2014, Number May (2014), Pages 1-13

Ubiquity symposium: MOOCs and technology to advance learning and learning research: data-driven learner modeling to understand and improve online learning
Kenneth R. Koedinger, Elizabeth A. McLaughlin, John C. Stamper
DOI: 10.1145/2591682

Advanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its width—more meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning.

In the midst of the recent high energy around massive open online courses (MOOCs) and other forms of online learning (e.g., Khan Academy), it is worthwhile to reflect on what these efforts may draw from and add to existing research in the learning sciences [1, 2]. Given that tens of thousands of students may complete a MOOC course, there is legitimate excitement about what we might learn from the great volumes of student interaction data that these courses are producing. However, for that excitement to become reality, computer scientists joining in this area will need to develop new expertise or forge collaborations with cognitive psychologists and educational data mining specialists.

We recommend data-driven learner modeling to understand and improve student learning. By data-driven learner modeling we mean the use of student interaction data to build explanatory models of elements of learning (e.g., cognition, metacognition, motivation) that can be used to drive instructional decision making toward better student learning. We frame this approach in contrast to the traditional pedagogical model employed in higher education and mimicked in MOOCs whereby "an expert faculty member's performance is the center of the course" [3].

This instructor-centered model typically includes, as do many MOOCs, questions to check for student understanding, problems to apply ideas in practice, and perhaps even learn-by-doing scenarios/simulations that require adapting concepts and skills in support of deeper learning. However, these activities are still "instructor-centered" in that they are primarily designed based on the intuitions of instructors, their conscious reflection on their expertise, and their beliefs about what students should know.

Our experience is that too much online course development is guided merely by instructor intuitions. These intuitions are clouded by what we have called "expert blind spot" [4]—the notion that experts are often unaware of the cognitive processes they utilize when performing in their specialty area. Much expertise is tacit knowledge used in pattern recognition, problem solving, and decision making, and experts' self-reflections are often inaccurate about the nature of their own tacit knowledge. For example, while most math educators judge story problems to be more difficult for beginning algebra students than matched equations [5], student data indicates the opposite: Students perform better on story problems (70 percent correct) than on matched equations (42 percent correct; 4). Therefore, in contrast to instructional design based purely on instructor intuition, course development should also be informed by the kind of data that has repeatedly revealed flaws and limitations in the models of student learning implicit in course designs.

Using data-driven models to develop and improve educational materials is fundamentally different from the instructor-centered model. In data-driven modeling, course development and improvement is based on data-driven analysis of student difficulties and of the target expertise the course is meant to produce; it is not based on instructor self-reflection as found in purely instructor-centered models. To be sure, instructors can and should contribute to interpreting data and making course redesign decisions, but should ideally do so with support of cognitive psychology expertise. Course improvement in data-driven modeling is also based on course-embedded in vivo experiments (multiple instructional designs randomly assigned to students in natural course use, also called "A/B testing") that evaluate the effect of alternative course designs on robust learning outcomes. In courses based on cognitive science and data-driven modeling, student interaction is less focused on reading or listening to an instructor's delivery of knowledge, but is primarily about students' learning by example, by doing and by explaining.

Successes in Data-Driven Course Improvement

Both qualitative and quantitative techniques that combine subject matter expertise with cognitive psychology have been developed and successfully applied to educational data in numerous domains. We provide several examples to illustrate how data can be used to inform instruction.

Cognitive task analysis based on qualitative analysis of verbal data. Cognitive task analysis (CTA) focuses on the psychological processes behind task performance. More specifically, CTA uses a variety of techniques to elicit the knowledge of experts and differentiate between the critical decision making of experts and novices. It is a proven method for discovering latent variables and unraveling some of the complexities of domain-specific learning. This method incorporates elements of cognitive psychology and domain expertise, and requires a high level of human interpretation of the data. By increasing the volume (e.g., as collected from MOOCs) and density (e.g., more frequent well-designed observations bearing on learners' cognitive and self-regulatory states) of data, the need for human interpretation and the potential for subjectivity can be reduced but it cannot be eliminated.

The success of various types of cognitive task analyses has been demonstrated in a variety of courses where newly discovered factors led to course modifications and better student learning. Velmahos et al. used a CTA with surgeons to make improvements to a course on catheter insertion for medical interns [6]. When compared with the pre-existing course, the data-driven course redesign resulted in higher posttest scores and better surgery results (e.g., 50 percent fewer needle insertions). Lovett employed a CTA with statistics' experts and discovered a "hidden skill" (variable type identification) as part of planning a statistical analysis [7]. We say "hidden" because expert instructors were not consciously aware of performing this planning step, nor were they aware of students' difficulty with it. In using data-driven insights like these, interactive activities were designed for students to practice such hidden skills [8]; these activities are also a key part of the Open Learning Initiative's online "Probability and Statistics" course. A randomized trial comparing blended use of this online course in a half semester to the preexisting full-semester course found students using the online course not only spent half the time learning, but learned more as demonstrated on a post-test measuring their conceptual understanding of statistics [8].

Cognitive task analysis based on quantitative analysis of educational technology data. Traditional CTA techniques use qualitative data (e.g., interviews with instructors and students) to assist in making pedagogical decisions for course improvement. We have developed quantitative approaches to conducting CTA that are more efficient and scalable. The data generated from observing student performance is used to discover hidden skills and support course improvements. Early work of this kind involved comparison of student performance on systematically designed task variations designed to pinpoint what tasks (problems/questions/activities) cause students the most difficulty. These so- called "difficulty factor assessments" have led to many discoveries, perhaps the most striking of which is, in contrast to math educators predictions, algebra students are actually better at solving story problems than matched equations [5]. Results such as these have been critical to the design and continual improvement of the "Cognitive Tutor Algebra" course, now in use by some 600,000 middle and high school students a year. The most recent of many full-year randomized field trials involved 147 schools and showed students using the "Cognitive Tutor Algebra" course learned twice as much as students in traditional algebra courses [9].

Heffernan and Koedinger employed a difficulty factors assessment that suggested the skill of composing multiple-operator expressions (e.g., as exercised in tasks like "substitute 40x for y in800-y") is, surprisingly, a hidden component of translating story problems to equations [10]. Koedinger and McLaughlin ran an in vivo study (an A/B test) replacing some story problem practice with such substitution tasks [11]. They demonstrated significantly better learning on complex story problem translation for students who had more opportunities to practice substitution than those who did not. Stamper and Koedinger used a learning curve analysis of geometry tutor data to discover a hidden planning skill on problems that cannot be solved by simply applying a single formula [12]. Koedinger, Stamper, McLaughlin, and Nixon redesigned the tutor based on this discovery and compared it with the prior tutor in an in vivo experiment [13]. Students using the redesigned tutor reached tutor-determined mastery in 25 percent less time and did better on a paper post-test, especially on difficult problems requiring the hidden planning skill that was discovered.

The previous examples are just a few illustrations of the power of using data to improve instruction. A key question than is how learning environments and data collection systems can be best designed to "yield data that transform into explanatory models of a student's learning, and also support course improvement" [3]? Having an understanding, an explanation, of how and why a model better predicts puts one in a much better position to use that understanding to make specific course redesigns.

Opportunities for Improving MOOCs

Before addressing the question of how to use data for improving MOOCs and courses in general, we first address the crucial question of designing learning activities to enhance data collection. Good course instrumentation for data gathering requires presenting complex tasks that represent learning objectives and identify students' intermediate thinking processes as they perform these difficult tasks. Using strategies that emphasize student activity and scaffold student reasoning processes will help improve the quality of data, improve the inferences that can be made from data, and thus lead to better instructional design decisions (e.g., instructional modifications, re-sequencing of tasks).

Data-gathering. To build explanatory and actionable models, we need data that is fine grained in time and in thinking units. Observations that are finer grained in time provide more gradable student actions per minute. Observations that are finer grained in thinking units help unpack how students are thinking, reasoning, or arriving at decisions. Many activities in MOOCs and online courses (e.g., multiple choice questions about simple facts) are too simple to provide much insight into student understanding and ability to apply what they have learned. Other activities are more complex, but only solicit final answers without recording intermediate reasoning steps. In Figure 1, for example, we see two entered answers (Parts A and B) for a complex physics problem without any of the steps, such as drawing the free body diagram or entering intermediate equations, leading up to these answers. The volume of such coarse grain data that are coming out of MOOCs will be of limited value, even if vast.

In Figure 2, we see a physics problem similar to the problem in Figure 1. However, in this case, students enter intermediate steps such as drawing vectors and writing a sequence of equations before offering a final solution. Such finer grained data provide more meaningful assessment beyond proficiency or completion, providing potential insights into aspects of reasoning or problem solving that are particularly challenging for students. Note in both examples students can make multiple (incorrect) attempts before arriving at a final (correct) solution.

Activities can be more finely instrumented by providing workspaces (e.g., a free body diagramming tool or an equation solving worksheet) as illustrated in Figure 2 or by adding scaffolding that prompts for intermediate solution products (e.g., asking for converted fractions before their final sum). Intelligent tutoring systems [14] and some online courses [8] do support this finer grain action collection and, further, add data about timing, correctness, and amount of instructional help needed. Such data are much more informative than simple single-answer correctness. It is precisely this kind of fine-grained, multi-featured observations that are found in datasets in DataShop [15].

Much like the intricacies of a CTA that uncover the cognitive processes behind an observable task, making student thinking visible in an online activity is about more than having them "show their work" as they would on paper. First, there is the challenge of designing an interface or AI technology such that the work is computer interpretable. Second, it is often desirable to have students indicate their thinking in ways that they might not normally do on paper. As an example of both, consider asking students to explain the steps they take in solving geometry problems (e.g., by entering "the sum of the angles of a triangle is 180" to justify that 70 is the value of a given angle). Prompting students to perform such "self-explanation" has been experimentally demonstrated to enhance learning in math and science domains. Computer interpretation of student explanations has been achieved through both structured interfaces (e.g., menus of alternate explanations) and natural language processing technology. Structured interfaces are effective in enhancing student learning [16], but it remains an open question whether the extra effort of implementing natural language processing leads to further learning gains [17].

Beyond black box models: From predictive to explanatory and actionable models. In addition to avoiding the pitfall of developing interactive activities that do not provide enough useful data to reveal student thinking, MOOC developers and data miners must avoid potential pitfalls in the analysis and use of data. One such pitfall is the application of sophisticated statistical and machine learning techniques to educational data without understanding or contributing to relevant cognitive and pedagogical principles. This "black box model" approach focuses on improving prediction without regards to understanding what is happening cognitively (i.e., inside the box). Such understanding provides a means for instructional improvement as we illustrate below. It requires using analysis methods that focus on developing explanatory models to produce interpretable insights. Such insights advance understanding of learning and produce recommendations for improved educational practices.

One step toward explanatory models is annotating data sets with theoretically motivated labels or semantic features. DataShop helps researchers label student actions, such as the steps in Figure 2, with factors that might cause students difficulties in doing or learning. For example, one such hypothesized difficulty is whether steps in a geometry tutor require students to apply an area formula "backwards" (i.e., when the area is given) rather than the usual forward application (i.e., finding the area). To our surprise, a model incorporating this distinction across all formulas did not predict student data better than one without this distinction. However, we then used a model discovery algorithm [19] to find a particular situation, the circle area formula, where making a backward-forward distinction did improve model prediction. We return to the question of why in a moment, but it is worth highlighting that this learning factors analysis (LFA) algorithm has been used on many DataShop data sets to discover better cognitive models of domain skills across a variety of domains (math, science, and language) and technologies (tutors, online courses, and educational games) [18]. LFA is an instance of a quantitative CTA discussed above.

Having well-labeled variables is important for developing explanatory models, but it is not sufficient. A second step toward explanatory and actionable models is applying psychological theory to interpret data-driven discoveries in terms of underlying cognitive processes. We started to interpret the better prediction of a model that splits, rather than merges, the forward versus backward applications of circle area by first verifying that the circle-area-to-radius (backward) steps were harder than circle-radius-to-area (forward) steps. An explanation, then, should indicate a cognitive process needed for the harder task that is not needed for the easier task: In this case, to undo the area formula (A = π r2) to find r requires knowing when to employ the square root operation. Finally, armed with such an explanation, a course developer can take action. The suggested action is to develop instruction and problems that better teach and practice the process of determining when to use the square root operation.

In general, more sophisticated algorithms need to be developed that unleash the potential instructional and learning benefits available from the big data obtained from MOOCs. But, to simply offer improved predictions (the standard goal in machine learning) without meaningful scientific discovery and practical implications is not sufficient. Instead, explanatory models of students are needed that uncover critical insights (e.g., that beginning algebra students are better at story problems than matched equations) or important nuances of student learning (e.g., that equations with "-x" terms are harder than ones with terms with numeric coefficients such as "3x") [19] so as to support improved instruction and learning.

A model that makes more accurate predictions may not be insightful or actionable. Conversely, a model that only produces small prediction improvements may nevertheless produce actionable insights. In fact, the models produced by LFA typically yield only small (but reliable) reductions in prediction error. Nevertheless, such models have been usefully interpreted to suggest modifications to improve educational materials. Randomized controlled experiments have demonstrated that such modifications can yield reliable and substantial improvements in student learning efficiency and post-instruction effectiveness [13].

Conclusion

There are great opportunities for improving MOOCs through data-driven learner modeling. However, the computer science community needs to better recognize and engage the existing state of knowledge in learning science and educational data mining. If not, the rich volume of principles of learning and instruction already produced by learning science research is at risk of being very slowly rediscovered by the new players to online course development. Educational data mining research has established a great potential for insights on student cognition, metacognition, motivation, and affect [20]. These insights have been possible only because the data used to derive them has come from student learning interactions that are both complex and fine-grained—of the kind produced in intelligent tutoring systems or other online activities involving multi-step interfaces (e.g., simulations, games, mini-tutors) [21]. Further, these insights have been used to make design changes in online systems and, in some cases, experiments have demonstrated significant improvements in student learning, metacognition, or motivation by comparing the redesigned system to the original one [13].

Using data-driven learner models to improve courses contrasts with the instructor-centered model in three key ways. First, course development and improvement is based not solely on instructor self-reflection, but on a data-driven analysis of student difficulties and of the target expertise the course is meant to produce. Second, course improvement is based on course-embedded in vivo experiments that evaluate the effect of alternative course designs on robust learning outcomes. Third, course interaction is not centrally about instructor's delivery knowledge, but about student learning by example, by doing and by explaining.

For data-driven learner modeling to yield greater understanding and improvement of student learning, we recommend more emphasis on (a) exploratory data analysis in addition to machine learning, (b) simpler models with fewer parameters as well as highly complex models, (c) use of explicit research questions to drive analyses, and (d) inclusion of cognitive psychology expertise to guide online activity designs that make thinking visible and to aid interpretation of model results.

More finely instrumented activities are not only valuable for making thinking visible and improving data for cognitive diagnosis, such fine-grain data are crucial to systems that are now making reliable inferences about students' motivations and affective states [20]. While the examples above have emphasized monitoring and analyzing cognitive functions, such as reasoning and problem solving, other educational data mining research has investigated roles of metacognition, motivation, and social dialogue in learning.

There are some good signs of recent progress in useful mining of MOOC data [22, 23], but more such work is needed. MOOCs and other forms of online learning provide a tremendous opportunity to enhance education and diversify learning if productive collaborations are formed and pitfalls of insufficient data-gathering and black box prediction are avoided. The more than 450 datasets already available in DataShop offer opportunities to develop data-driven models of learners that include conceptual understanding, cognitive skills, metacognitive and learning skills, general dispositions and motivations toward learning, and specific states of affect (e.g., confusion or flow) during learning [15]. These data-driven learner models provide great potential to advance both learning science and educational practice.

References

[1] Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., and Metcalfe, J. Organizing Instruction and Study to Improve Student Learning (NCER 2007–2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. 2007.

[2] Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., and Norman, M. K. How Learning Works: Seven Research-Based Principles for Smart Teaching. Jossey-Bass, San Francisco, 2010.

[3] Thille, C. Opening statement for symposium: Technology to Advance Learning and Learning Research. ACM Ubiquity April 2004.

[4] Koedinger, K. R. and Nathan, M. J. The real story behind story problems: Effects of representations on quantitative reasoning. The Journal of the Learning Sciences 13, 2 (2004), 129–164.

[5] Nathan, M. J. and Koedinger, K. R. An investigation of teachers' beliefs of students' algebra development. Cognition and Instruction 18, 2 (2000), 207–235.

[6] Velmahos, G. C., Toutouzas, K. G., Sillin, L. F., Chan, L., Clark, R. E., Theodorou, D., and Maupin, F. Cognitive task analysis for teaching technical skills in an inanimate surgical skills laboratory. The American Journal of Surgery 18 (2004), 114–119.

[7] Lovett, M. C. Cognitive task analysis in service of intelligent tutoring systems design: A case study in statistics. In B. P. Goettl, H. M. Halff, C. L. Redfield, and V. J. Shute (Eds.) Intelligent Tutoring Systems, Lecture Notes in Computer Science Volume 1452. Springer, New York, 1998, 234–243.

[8] Lovett, M., Meyer, O., Thille, C. The Open Learning Initiative: Measuring the effectiveness of the OLI learning course in accelerating student learning. Journal of Interactive Media in Education. 2008.

[9] Pane, J. F., Griffin, B. A., McCaffrey, D. F., Karam, R. Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis. 2013. Published online 11 November 2013 as doi:10.3102/0162373713507480.

[10] Heffernan, N. and Koedinger, K. R. A developmental model for algebra symbolization: The results of a difficulty factors assessment. In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ, 1998, 484–489.

[11] Koedinger, K.R. and McLaughlin, E.A. Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society (2010, Austin). 471–476.

[12] Stamper, J. and Koedinger, K.R. Human-machine student model discovery and improvement using data. In Proceedings of the 15th International Conference on Artificial Intelligence in Education. 2011.

[13] Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., and Nixon, T. Using data-driven discovery of better student models to improve student learning. The 16th International Conference on Artificial Intelligence in Education (AIED2013). 2013.

[14] VanLehn, K. The behavior of tutoring systems. International Journal of Artificial Intelligence in Education 16, 3 (2006), 227–265.

[15] Koedinger, K.R., Baker, R.S.J.D., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. A Data Repository for the EDM community: The PSLC DataShop. In Romero, Ventura, Pechenizkiy, Baker, (Eds.) Handbook of Educational Data Mining. CRC Press, 2010.

[16] Aleven, V. A., and Koedinger, K. R. An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science 26, 2 (2002).

[17] Aleven V., Koedinger, K. R., and Popescu, O. A tutorial dialog system to support self-explanation: Evaluation and open questions. In U. Hoppe, F. Verdejo, and J. Kay (Eds.), Proceedings of the 11th International Conference on Artificial Intelligence in Education, AI-ED 2003. IOS Press, Amsterdam 2003, 39–46.

[18] Li, N., Stampfer, E., Cohen, W.W., and Koedinger, K.R. General and efficient cognitive model discovery using a simulated student. M. Knauff, M. Pauen, N. Sebanz, and I. Wachsmuth (Eds.). In Proceedings of the 35th Annual Conference of the Cognitive Science Society. 2013.

[19] Koedinger, K. R., McLaughlin, E. A., and Stamper, J. C. Automated Student Model Improvement. Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (eds.) Proceedings of the 5th International Conference on Educational Data Mining (June 2012, Chania, Greece).

[20] Baker, R.S., Corbett, A.T., and Koedinger, K.R. Detecting Student Misuse of Intelligent Tutoring Systems. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems. (Aug 2004, Maceio, Brazil). 531–540.

[21] Koedinger, K. R., Brunskill, E., S.J.D. Baker, R., McLaughlin, E. A., and Stamper, J. C. (Submitted). New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization. Artificial Intelligence Magazine.

[22] Huang, J., Piech, C., Nguyen, A., and Guibas, L. Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC. In Proceedings of the 1st Workshop on Massive Open Online Courses at the 16th Annual Conference on Artificial Intelligence in Education (July 2013, Memphis, TN).

[23] Piech, C., Huang, J., Chen, Z., Do, C., Ng, A., and Koller, D. Tuned Models of Peer Assessment in MOOCs. D'Mello, S. K., Calvo, R. A., and Olney, A. (eds.) In Proceedings of the 6th International Conference on Educational Data Mining (July 2013, Memphis, TN). 153–160).

Authors

Ken Koedinger is Professor of Human-Computer Interaction and Psychology at Carnegie Mellon. He directs LearnLab, which leverages cognitive and computational approaches to support researchers in investigating the instructional conditions that cause robust student learning.

Elizabeth A. McLaughlin is a Research Associate at the Human-Computer Interaction Institute at Carnegie Mellon University.

John Stamper is a member of the research faculty at the Human-Computer Interaction Institute at Carnegie Mellon University and is the Technical Director of LearnLab's DataShop.

Figures

F1Figure 1. Coarse-grain data collection is illustrated in an online physics homework system called "MasteringPhysics," where students use the keyboard to enter a final answer to a problem (e.g., in Part B an incorrect expression for the magnitude of a force Fww is provided by a student). In a single activity (a problem to solve), just two gradable student steps are observed and stored in the data log for later analysis. Source: Pearson MasteringPhysics.

F2Figure 2. Fine-grain data collection is illustrated in a physics intelligent tutor, Andes, where students use mouse clicks and the keyboard to draw diagrams (e.g., the coordinates and vectors are drawn over the given problem image in the middle left); define quantities (e.g., the variable T0 is defined as "the instant depicted" in the upper right); and enter equations (e.g., a first equation, "Fg_y+F1_y=0", which is incorrect, is shown in the middle right). In a single activity (a problem to solve), about 20 gradable student steps are observed live by the tutoring system, and stored in the data log for later analysis. Source: The Andes Project.

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