In this study, we explored the impact of the decision agency(Student vs. Tutor) and granularity (Problem vs. Step) acrossstudents with different levels of incoming competence (Highvs. Low). Students were randomly assigned to four conditionsand split into High and Low groups based on their pre-testscores. All students used the same Intelligent Tutoring Sys-tem (ITS) called Pyrenees, followed the same general proce-dure, studied the same training materials, and worked throughthe same training problems. The only substantive differencesamong the four conditions were decision agency and granular-ity. That is: who decided to present an example or to solvea problem: the student or the ITS tutor; and was the deci-sion made problem-by-problem or step-by-step? Our overallresults showed that there were significant different impacts ofthe decision agency and granularity between High and Lowstudents on learning performance. More specifically, for Highstudents granularity was the more dominant factor in that steplevel decisions can be more effective than problem level deci-sions regardless of the decision agency; for Low students therewas a significant interaction effect in that: Low students ben-efit significantly more when they were making problem-leveldecisions than making step-level decisions, but no significantdifference was not found when the decisions were made bythe tutor. Much to our surprise, both High and Low groupsshowed strong decision-making preference for problem solv-ing over worked example at both problem and step levels.

We explored the impact of three types of decision granularity,problem level (Prob), step level (Step), and both problem andstep levels (Both), on student learning. We first conducted anempirical study to directly compare the three conditions andthen three subsequent studies to evaluate one or two of thethree conditions. Overall our empirical results showed therewas no significant difference among the three conditions. Wefurther split students into different groups based on their per-formances on the single-principle and the multiple-principleproblems in the pre-test. Solving the single-principle problemsonly involves one step while solving the multiple-principleones involves generating multiple steps in a logic order. Wedefine High students as those who were correct on all single-principle problems and at least one multiple-principle ones inthe pre-test, Low students as those who were correct on someor all single-principle problems but no multiple-principle ones,and the rest are in the Medium group. Our empirical resultsshowed that for Low students, Both can be better than Step.For the Medium and High students, no clear conclusions couldbe drawn because of small sample sizes. As a result, in apost-hoc analysis all students were combined by their assignedconditions. Overall, while no significant difference was foundamong the three conditions, we found that the impact of threetypes of granularity, Prob, Step, and Both differs significantlyfor High vs. Low students: Both, Step > Prob for the Highstudents and Both, Prob > Step for the Low students. No clearconclusions could be drawn for the Medium group due to itssmall sample sizes. In short, while Prob could be effective forLow students but ineffective for High ones and Step could beeffective for High students but ineffective for Low ones, Bothseemed to be effective for both High and Low students.

In this work, we investigate how two factors, metacognitiveskills and motivation, would impact student learning acrossdomains. More specifically, our primary goal is to identify thecritical, yet robust, interaction patterns of these two factors thatwould contribute to students’ performance in learning logicfirst and then their performance on a subsequent new domain,probability. We are concerned with two types of metacognitiveskills: strategy-awareness and time-awareness, that is, whichproblem-solving strategy to use and when to use it. Our datawere collected from 495 participants across three consecutivesemesters, and our results show that the only students who con-sistently outperform their peers across both domains are thosewho are not only highly motivated but also strategy-aware andtime-aware.

In this study we explored the impact of student versus tutorpedagogical decision-making on learning. More specifically,we examined what would happen if we let students decidehow to handle the next task: to view it as a worked exam-ple or to solve it as a problem solving. We examined this im-pact at two levels of task granularity: problem vs. step. This2 × 2 study was conducted on an existing Intelligent TutoringSystem (ITS) called Pyrenees. 279 students were randomlyassigned to four conditions and the domain content and re-quired steps were strictly controlled to be equivalent acrossfour conditions: all students used the same system, followedthe same general procedure, studied the same training materi-als, and worked through the same training problems. The onlysubstantive differences among the four conditions were deci-sion agency {Student vs. Tutor} and granularity {Problem vs.Step}. That is: who decided to present an example or to solvea problem; and was the decision made problem-by-problem orstep-by-step? Our results showed that there was a significantinteraction effect between decision agency and granularity onstudent learning and a significant main effect of granularity ontime on training. That is, step level decisions can be more ef-fective than problem level decisions but the students were morelikely to make effective pedagogical decisions at problem levelthan step level. In general, on both problem and step levels, thestudents were significantly more likely to decide to do problemsolving rather than study it as a worked example.

In this paper, we explore the impact of two types of instructional interventions, worked examples and problem solving, at two levels of granularity: problems and steps. This study drew on an existing Intelligent Tutoring System (ITS) for Probability called Pyrenees and involved 266 students who were randomly assigned to five conditions. All students experienced the same procedure, studied the same training problems in the same order, and used the same ITS. The conditions differed only in how the training problems were presented. Our results show that when the domain content and required steps are strictly equivalent, different granularities of pedagogical decisions can significantly impact students’ time on task. More specifically, the fine-grained step level decisions can have a stronger pedagogical impact than the problem-level ones.