With an increasing focus on science and technology in education comes an awareness that students must be able to understand and integrate scientific explanations from multiple sources. As part of a larger project aimed at deepening our understanding of student processes for integrating multiple sources of information, we are developing machine learning and natural language processing techniques for evaluating students’ argumentative essays. In previous work, we have focused on identifying conceptual elements of the essays. In this paper, we present a method for inferring the causal structure of student essays. We used a standard parser to derive grammatical dependencies of the essay and converted them to logic statements. Then a simple inference mechanism was used to identify concepts linked to syntactic connectors by these dependencies. The results suggest that we will soon be able to provide explicit feedback that enables teachers and students to improve comprehension.

Operation Aries! is a computer environment that helps students learn about scientific methods and inquiry. The system has several components designed to optimize learning and motivation, such as game features, animated agents, natural language communication, trialogues among agents, an eBook, multimedia, and formative assessment. The present focus is on a Case Study learning module that involves critiquing reports of scientific findings in news media that have flawed scientific methodology. After the human student lists the methodological flaws of a Case Study in natural language, a teacher agent and a peer agent hold a trialogue with the student that evaluates each listed flaw and that uncovers additional flaws that that student missed.

We will demonstrate two Intelligent Tutoring Systems (ITSs) that aspire to teach scientific inquiry skills though a natural language conversational dialogue. The ITSs include a new version of the AutoTutor system called Criti- cal Thinking Tutor and ARIES (Acquiring Research Investigative and Evalua- tive Skills), which is a semester long tutorial intervention that teaches scientific inquiry skills via a trialogue between two animated pedagogical agents (APAs) and a human learner. The systems provide cases that mirror authentic scientific research and allow users to evaluate the studies by assuming various roles (stu- dent, teacher, judge, jury), posing questions, and offering critiques.

Consider the assignment that teachers have been giving their students for years: “Write an expository essay on a scientific topic. Example topics may include global warming, human memory, or the spread of infectious diseases. You must have at least three references.” The instructor makes it clear that the paper should have a thesis or claim that is supported by evidence. Claims might be that global warming will be disastrous only for some nations, why it is futile to teach mnemonics to young children, or that cell phone use causes cancer. From the perspective of the student (and cognitive psychologists), this assignment is challenging at any grade. The challenge is that the assignment entails a number of complicated and interconnected tasks. For example, reading a research paper requires the reader to make inferences that span sentences and paragraphs (in addition to a whole host of other processes), and to understand the logical and rhetorical structure of the text as a whole. If the paper describes an experiment, the student must additionally understand how to determine whether the data support the conclusion (i.e., the scientific method). In most cases, the student must also integrate the content of several papers (sources) into a coherent structure. This process involves evaluating the credibility of the sources, selecting relevant pieces of information from each, and putting them into a coherent argument structure. No wonder such assignments are met with groans.