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Instructions for ACL-2013 Proceedings Jaime Carbonell as both SCHOLAR and TEACHER Carolyn Penstein Rosé Language Technologies Institute and Human-Computer Interaction Institute 5000 Forbes Avenue Pittsburgh, PA 15213 [email protected] Schank, namely Janet Kolodner, who eventually Abstract became the Matriarch of the Learning Sciences and a close colleague of mine to this day, and “It is often said of Jaime Carbonell that one Richard Granger, who I already mentioned was meeting with him is worth ten with anyone my second mentor at UC Irvine, and instructor of else. Though my interactions with Jaime were my first graduate level Machine Learning course few, I can honestly say they were worth the in Spring of 1990. It has clearly been to the great wait.” advantage of many that Roger Schank invested his energy mentoring these three. In the remain- Quote from the Acknowledgements of one of the first Language Technologies Institute dis- der of this article, an outline will be provided of sertations (Rosé, 1997) three different areas of online education where Jaime’s work has made an impact, beginning 1 Introduction with research in Tutorial Dialogue, then Com- puter Supported Collaborative Learning, and While Jaime is best known for his tremendous most recently work in the area of Massively contributions and impact in the areas of Natural Open Online Courses (MOOCs). Each of these Language Processing, Artificial Intelligence, and discussions highlights a connection with and Machine Learning, his impact has also been benefit from Jaime and his work. keenly felt in the area of technology supported education. 2 Tutorial Dialogue As a personal note, my first introduction to this area came during a survey course on re- In Alfred Bork’s work on tutorial at the Universi- search in computer science at the University of ty of California at Irvine in collaboration with the California at Irvine, where I was an undergradu- University of Geneva in the late 80s, the concept ate in the late 80s. As I carefully considered my of tutorial dialogue was a key technology used to options for undergraduate research in the Com- engage students in valuable learning experiences puter Science honors program, I was torn be- in a variety of educational domains. The concept tween working with Alfred Bork on technology was not new, however. The idea of tutorial dia- supported education and Richard Granger on logue systems was conceived over a decade ear- computational neuroscience. Not able to choose lier, by both Jaime and his father, also named between the first, which seemed to have greater Jaime Carbonell, in their work on the landmark potential for human impact, and the second, SCHOLAR system (Carbonell, 1969; Carbonell, which seemed more intellectually stimulating, I 1970). The seeds were sewn in this project chose to pursue both. Little did I know at that work with Alfred Bork that eventually inspired point that both paths would ultimately point back research towards development of more robust to Jaime’s seminal work and career debut, begin- technology for dialogic interactions between ning with research Jaime did during his graduate computer agents and humans in the next decade school years in Roger Schank’s group at Yale (Rosé, 1997). Bork and his contemporaries (Carbonell, 1969; Carbonell, 1970; Collins et al., taught that the biggest road block to impact in 1973; Carbonell, 1977). that area was that the technology for understand- While Jaime was busy sowing the seeds ing human language was too brittle. Upon fur- of impact with his own research contributions ther reflection and digging deeper into the theo- during those years, he was enjoying the company retical underpinnings of the field of Learning of two office mates and fellow students of Roger Sciences, the naiveté of that belief eventually came to light. Nevertheless, this taste of research 1997), achieving the goal of the full extent of the paved the way for pursuing a Ph.D. in Computa- effectiveness of expert human tutors remains elu- tional Linguistics, to develop technology for ro- sive to this day. The search for the answer to this bust language understanding. And what better mystery has taken many forms, but one common place to do that than Carnegie Mellon University, thread through generations of investigation has where the opportunity presented itself to earn a been the belief that the answer lies in the natural Master’s degree in Computational Linguistics language dialogue that is the dominant form of and then a Ph.D. in Language and Information communication between students and human Technologies. At that time, tutorial dialogue was tutors, and especially in adopting a Socratic tu- not a major area of language technologies, so toring style where students are lead to construct there were not opportunities to work directly in knowledge for themselves through directed ques- that area. In fact, it was sometimes said that one tioning (Carbonell, 1969; Rosé et al., 2001b). who followed that path might not find a job. Early efforts to emulate the effectiveness of hu- However, upon completing my dissertation at the man tutorial dialogue, such as the SCHOLAR end of the 90s when the field of Language Tech- system (Carbonell, 1969; Carbonell, 1970) and nologies was undergoing a great paradigm shift, the original WHY system (Stevens & Collins, the field of Learning Sciences was experiencing 1977), were often acknowledged as the landmark its own paradigm shift, and a rebirth of interest in systems in the history of intelligent tutoring re- Tutorial Dialogue systems. Thus, at the perfect search. Nevertheless, it was acknowledged that time, an opening to pursue this research present- in that early work the conception of what Socrat- ed itself at the Learning Research and Develop- ic tutoring is and why it should be effective was ment Center (LRDC) at the University of Pitts- not sufficiently well worked out, and the tech- burgh, where there were also opportunities to nology to support such interactions was not yet benefit from mentoring and instruction from mature. great leaders in Education, Cognitive Science, The work at the Learning Research and De- and Educational Psychology such as Alan velopment Center at that time in some ways Lesgold, Lauren Resnick, Johanna Moore, Kurt picked up where Jaime’s work had left off. In VanLehn, and Michelene Chi. one thread of work, I developed tools for effi- In this context one is frequently reminded of ciently constructing robust tutorial dialogue Jaime and his earlier work on SCHOLAR. For agents capable of leading students through di- example, the goal of the WHY2 project rected lines of reasoning, initially in a physics (VanLehn et al., 2002; Rosé & VanLehn, 2005; instruction context (Freedman et al., 2000; Rosé, VanLehn et al., 2007) was to focus specifically 2000; Jordan, Rosé, & VanLehn, 2001; Rosé et on conceptual physics problems and support stu- al., 2002; Rosé et al., 2005a). These tools ena- dents in developing the skills to articulate multi- bled a series of successful evaluations in real step conceptual physics explanations. As in all classrooms (Rosé et al., 2001; Rosé et al., 2005b; scholarly work, we acknowledge the lineage of Kumar et al., 2006). Nevertheless, it eventually our ideas and efforts. We often harkened back to became clear that one major road block to the ways in which that work grew out of a long achieving impact with the technology was that term and multi-faceted effort to emulate in intel- student expectations of computer agents acted as ligent tutoring technology the elements that were a hindrance to them interacting with the agents in believed to make human tutoring such an effec- instructionally beneficial ways, regardless of the tive form of instruction. Expert human tutors technical capabilities of such agents (Rosé & were known to be highly successful at educating Torrey, 2005). It also became clear that even students (Bloom, 1984; Cohen, Kulik & Kulik, human tutoring wasn’t always as effective as 1982). Students working with an expert human claimed in the earlier Cohen and Bloom studies tutor were thought to achieve a learning gain of (VanLehn et al., 2007). Thus, attention turned to up to two standard deviations above those in a the use of conversational agents as facilitators in regular classroom setting. Emulating this “2 sig- collaborative learning interactions, where the ma effect” has long been the holy grail of intelli- richness of human interaction could be experi- gent tutoring research. While great strides in de- enced through peer interactions, which also have veloping instructional technology had been made some benefits from a Piagetian theoretical per- by that time, especially in the area of building spective. In this environment, correct content coached problem solving practice environments and guidance could be provided by carefully de- (Gertner & VanLehn, 2000; Koedinger et al., signed agents. 3 Computer Supported Collaborative based support, a more dynamic and potentially Learning more desirable approach would be to trigger support based on observed need and to fade scaf- One of the first projects I worked on with Jaime folding over time as students acquire the skills when returning to the Language Technologies needed to collaborate productively in a learning Institute in the early part of the century was to context. co-advise a student on work focusing on auto- The concept of adaptive collaborative learning mated collaborative learning process analysis support was first evaluated in a Wizard-of-Oz (Donmez et al., 2005), bringing together a com- setup and found to be effective for supporting mon interest in technology supported education learning (Gweon, Rosé, Zaiss, & Carey, 2006). and machine learning. Indeed, there had been As a further proof of concept of the feasibility much work in the computer supported collabora- and potential impact of such an approach, one tive learning community on modeling the process former LTI Ph.D.
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