Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.
In the proceeding of 24th International Conference OF Artificial Intelligence in Education (AIED)
Humans adopt various problem-solving strategies depending
on their mastery level, problem type, and complexity. Many of
these problem-solving strategies have been integrated within intelligent
problem-solvers to solve structured and complex problems efficiently.
One such strategy is the means-ends analysis which involves comparing
the goal and the givens of a problem and iteratively setting up subgoal(s)
at each step until the subgoal(s) are straightforward to derive from the
givens. However, little is known about the impact of explicitly teaching
novices such a strategy for structured problem-solving with tutors. In
this study, we teach novices a subgoal-directed problem-solving strategy
inspired by means-ends analysis using a problem-based training intervention
within an intelligent logic-proof tutor. As we analyzed students’
performance and problem-solving approaches after training, we observed
that the students who learned the strategy used it more when solving
new problems, constructed optimal logic proofs, and outperformed those
who did not learn the strategy.
more »
« less
- Award ID(s):
- 2013502
- PAR ID:
- 10525848
- Publisher / Repository:
- Springer
- Date Published:
- Format(s):
- Medium: X
- Location:
- In the proceeding of 24th International Conference OF Artificial Intelligence in Education (AIED)
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
The assistance dilemma is a well-recognized challenge to determine when and how to provide help during problem solving in intelligent tutoring systems. This dilemma is particularly challenging to address in domains such as logic proofs, where problems can be solved in a variety of ways. In this study, we investigate two data-driven techniques to address the when and how of the assistance dilemma, combining a model that predicts \textit{when} students need help learning efficient strategies, and hints that suggest \textit{what} subgoal to achieve. We conduct a study assessing the impact of the new pedagogical policy against a control policy without these adaptive components. We found empirical evidence which suggests that showing subgoals in training problems upon predictions of the model helped the students who needed it most and improved test performance when compared to their control peers. Our key findings include significantly fewer steps in posttest problem solutions for students with low prior proficiency and significantly reduced help avoidance for all students in training.more » « less
-
Problem decomposition into sub-problems or subgoals and recomposition of the solutions to the subgoals into one complete solution is a common strategy to reduce difficulties in structured problem solving. In this study, we use a datadriven graph-mining-based method to decompose historical student solutions of logic-proof problems into Chunks. We design a new problem type where we present these chunks in a Parsons Problem fashion and asked students to reconstruct the complete solution from the chunks. We incorporated these problems within an intelligent logic tutor and called them Chunky Parsons Problems (CPP). These problems demonstrate the process of problem decomposition to students and require them to pay attention to the decomposed solution while they reconstruct the complete solution. The aim of introducing CPP was to improve students’ problem-solving skills and performance by improving their decomposition-recomposition skills without significantly increasing training difficulty. Our analysis showed that CPPs could be as easy as Worked Examples (WE). And, students who received CPP with simple explanations attached to the chunks had marginally higher scores than those who received CPPs without explanation or did not receive them. Also, the normalized learning gain of these students shifted more towards the positive side than other students. Finally, as we looked into their proof-construction traces in posttest problems, we observed them to form identifiable chunks aligned with those found in historical solutions with higher efficiency.more » « less
-
null (Ed.)Subgoal labels are function-based instructional explanations that describe the problem-solving steps to the learner, highlighting the solution process. There is strong evidence that the use of subgoal labels within worked examples improves student learning in other STEM fields. Initial research shows that using subgoal labels within computer science improves student learning, but this has only been tested using a single programming concept (indefinite loops) with text-based programming languages. The proposers are currently expanding subgoal labels to the main programming concepts taught in an introductory programming course using an imperative programming language. In this BOF we seek to uncover tacit knowledge that programming instructors have in order to develop instructional materials that bridge the gap between students, who are CS novices, and instructors, who are CS experts, to improve learning for students who are under-prepared for or struggle in CS1. We will be seeking feedback on the selection of programming topics to be covered, the defined subgoals for those topics and the worked examples created for instructional purposes.more » « less
-
In this paper, we explore using Parsons problems to scaffold novice programmers who are struggling while solving write-code problems. Parsons problems, in which students put mixed-up code blocks in order, can be created quickly and already serve thousands of students while other types of programming support methods are expensive to develop or do not scale. We conducted two studies in which novices were given equivalent Parsons problems as optional scaffolding while solving write-code problems. We investigated when, why, and how students used the Parsons problems as well as their perceptions of the benefits and challenges. A think-aloud observational study with 11 undergraduate students showed that students utilized the Parsons problem before writing a solution to get ideas about where to start; during writing a solution when they were stuck; and after writing a solution to debug errors and look for better strategies. Semi-structured interviews with the same 11 undergraduate students provided evidence that using Parsons problems to scaffold write-code problems helped students to reduce the difficulty, reduce the problem completion time, learn problem-solving strategies, and refine their programming knowledge. However, some students found them less useful if the Parsons solution did not match their approach or if they did not understand the solution. We then conducted a between-subjects classroom study with 81 undergraduate students to investigate the effects on learning. We found that students who received Parsons problems as scaffolding during write-code problems spent significantly less time solving those problems. However, there was no significant learning gain in either condition from pretest to posttest. We also discuss the design implications of our findings.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Proceeding:
- The DOI is not currently available.
