Scaffolding Strategy In Mathematics Learning

Mathematics learning should be designed so that it is oriented to the goal which is focused on process. Learning orientation is focused on the development of ‘mathematical thinking’ and ‘mathematical disposition’. Then, the teaching-learning process is conditioned in order that student is actively to construct “meaning” through the process of self-experience, not just knowing. In such conditions the teacher's role shifted from mere "showing and telling" to be facilitators and guidance who can be responsive to students' development of thinking processes which proceed from the actual ability toward potential ability to construct mathematical knowledge. These efforts, among others, carried out by presenting a learning construction which is appropriate with the tradition of socio-constructivism. One of the most important tradition of socio-constructivism is the idea of scaffolding in practice learning. This study describe the role and strategy of scaffolding in mathematics learning. Key words: Scaffolding Strategy, Mathematics Learning

Supporting Constructive Learning with a Feedback Planner

A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from robotics and computational linguistics. We can use reactive planning techniques from robotics to allow us to modify tutorial plans, adapting them to student input. Computational linguistics will give us guidance in handling communication management as well as building a reusable architecture for tutorial dialogue systems. A modular and reusable architecture is critical given the difficulty in constructing tutorial dialogue systems and the many domains to which we would like to apply them. In this paper, we propose such an architecture and discuss how a reactive planner in the context of this architecture can implement multi-turn tutorial strategies

Boredom and student modeling in intelligent tutoring systems

Over the past couple decades, intelligent tutoring systems (ITSs) have become popular in education. ITSs are effective at helping students learn (VanLehn, 2011; Razzaq, Mendicino & Heffernan, 2008; Koedinger et al, 1997) and help researchers understand how students learn. Such research has included modeling how students learn (Corbett & Anderson, 1995), the effectiveness of help given within an ITS (Beck et al, 2008), the difficulty of different problems (Pardos & Heffernan, 2011), and predicting long-term outcomes like college attendance (San Pedro et al, 2013a), among many other studies. While most studies have focused on ITSs from a cognitive perspective, a growing number of researchers are paying attention to the motivational and affective aspects of tutoring, which have been recognized as important components of human tutoring (Lepper et al, 1993). Recent work has shown that student affect within an ITS can be detected, even without physical sensors or cameras (D’Mello et al, 2008; Conati & Maclaren, 2009; Sabourin et al, 2011; San Pedro et al, 2013b). Initial studies with these sensor-less affect detectors have shown that certain problematic affective states, such as boredom, confusion and frustration, are prevalent within ITSs (Baker et al, 2010b). Boredom in particular has been linked to negative learning outcomes (Pekrun et al, 2010; Farmer & Sundberg, 1986) and long-term disengagement (Farrell, 1988). Therefore, reducing or responding effectively to these affective states within ITSs may improve both short- and long-term learning outcomes. This work is an initial attempt to determine what causes boredom in ITSs. First, we determine which is more responsible for boredom in ITSs: the content in the system, or the students themselves. Based on the findings of that analysis, we conduct a randomized controlled trial to determine the effects of monotony on student boredom. In addition to the work on boredom, we also perform analyses that concern student modeling, specifically how to improve Knowledge Tracing (Corbett & Anderson, 1995), a popular student model used extensively in real systems like the Cognitive Tutors (Koedinger et al, 1997) and in educational research

When practice does not make perfect: Differentiating between productive and unproductive persistence

Research has suggested that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning—a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills—has shown that not all persistence is uniformly beneficial for learning. For this reason, Study 1 used educational data-mining techniques to determine key differences between the behaviors associated with productive persistence and wheel-spinning in ASSISTments, an online math learning platform. This study’s results indicated that three features differentiated between these two modes of persistence: the number of hints requested in any problem, the number of bottom-out hints in the last eight problems, and the variation in the delay between solving problems of the same skill. These findings suggested that focusing on number of hints can provide insight into which students are struggling, and encouraging students to engage in longer delays between problem solving is likely helpful to reduce their wheel-spinning. Using the same definition of productive persistence in Study 1, Study 2 attempted to investigate the relationship between productive persistence and grit using Duckworth and Quinn’s (2009) Short Grit Scale. Correlational results showed that the two constructs were not significantly correlated with each other, providing implications for synthesizing literature on student persistence across computer-based learning environments and traditional classrooms

Exploring the visualization of student behavior in interactive learning environments

My research combines Interactive Learning Environments (ILE), Educational Data Mining (EDM) and Information Visualization (Info-Vis) to inform analysts, educators and researchers about user behavior in software, specifically in CBEs, which include intelligent tutoring systems, computer aided instruction tools, and educational games. InVis is a novel visualization technique and tool I created for exploring, navigating, and understanding user interaction data. InVis reads in user-interaction data logged from students using educational systems and constructs an Interaction Network from those logs. Using this data InVis provides an interactive environment to allow instructors and education researchers to navigate and explore to build new insights and discoveries about student learning. I conducted a three-point user study, which included a quantitative task analysis, qualitative feedback, and a validated usability survey. Through this study, I show that creating an Interaction Network and visualizing it with InVis is an effective means of providing information to users about student behavior. In addition to this, I also provide four use-cases describing how InVis has been used to confirm hypotheses and debug software tutors. A major challenge in visualizing and exploring the Interaction Network is network's complexity, there are too many nodes and edges presented to understand the data efficiently. In a typical Interaction Network for twenty students, it is common to have hundreds of nodes, which to make sense of, has proven to be too many. I present a network reduction method, based on edge frequencies, which lowers the number of edges and nodes by roughly 90\\% while maintaining the most important elements of the Interaction Network. Next, I compare the results of this method with three alternative approaches and show our reduction method produces the preferred results. I also present an ordering detection method for identifying solution path redundancy because of student action orders. This method reduces the number of nodes and edges further and advances the resulting network towards the structure of a simple graph. Understanding the successful student solutions is only a portion of the behaviors we are interested in as researchers and educators using computer based educational systems, student difficulties are also important. To address areas of student difficulty, I present three different methods and two visual representations to draw the attention of the user to nodes where students had difficulty. Those methods include presenting the nodes with the highest number of successful students, the nodes with the highest number of failing students, and the expected difficulty of each state. Combined with a visual representation, these methods can draw the focus of users to potentially important nodes, which contain areas of difficulty for students. Lastly, I present the latest version of the InVis tool, which is a platform for investigating student behavior in computer based educational systems. Through the continued use of this tool, new researchers can investigate many new hypotheses, research questions and student behaviors, with the potential to facilitate a wide range of new discoveries