Novice programmers need differentiated assessments (such as adaptive Parsons problems) to maximize their ability to learn how to program. Parsons problems require learners to place mixed-up code blocks in the correct order to solve a problem. We conducted a within-subjects experiment to compare the efficiency and cognitive load of solving adaptive Parsons problems versus writing the equivalent (isomorphic) code. Undergraduates were usually more significantly efficient at solving a Parsons problem than writing the equivalent code, but not when the solution to the Parsons problem was unusual. This has implications for problem creators. This paper also reports on the mean cognitive load ratings of the two problem types and the relationship between efficiency and cognitive load ratings. Lastly, it reports on think-aloud observations of 11 students solving both adaptive Parsons problems and write-code problems and the results from an end-of-course student survey.

Dynamically adaptive Parsons problems are pieces of code that must be ordered and indented correctly. Performance on prior problems determines the difficulty of subsequent ones. These problems comprise an adaptive learning system that dynamically adapts the learning experience to an individual's ability. But how do novice programmers with learning (dis)abilities experience solving them? And, how can we make this learning experience more accessible? To understand the learning experience, researchers suggest testing the hypothesis that cognitive load is less for solving dynamically adaptive Parsons problems than writing equivalent code. Furthermore, researchers propose using an ability-based design approach to increase the accessibility of adaptive learning systems such as dynamically adaptive Parsons problems.

Learning technologies are generating a vast quantity of data every day. This data is often presented to students through learning analytics dashboards (LADs) with a goal of improving learners' self-regulated learning. However, are students actually using these dashboards, and do they perceive that using dashboards lead to any changes in their behavior? In this paper we report on the development and implementation of several dashboard views, which we call My Learning Analytics (MyLA). This study found that students thought using the dashboard would have more of an effect on the way they planned their course activity at pre-use (after a demo) than post use. Low self-regulated learners believed so significantly less post-use and used the grade distribution view the least. Students made several suggestions for ways to improve the grade distribution view and rated MyLA's usability more positively at pre- than post-use. Given the low use and low perceived impact of the current dashboard, we suggest that researchers use participatory design to illicit students' needs and better incorporate student suggestions.

Users make frequent use of Web search for learning-related tasks, but little is known about how different Web search interaction strategies affect outcomes for learning-oriented tasks, or what implicit or explicit indicators could reliably be used to assess search-related learning on the Web. We describe a lab-based user study in which we investigated potential indicators of learning in web searching, effective query strategies for learning, and the relationship between search behavior and learning outcomes. Using questionnaires, analysis of written responses to knowledge prompts, and search log data, we found that searchers' perceived learning outcomes closely matched their actual learning outcomes; that the amount searchers wrote in post-search questionnaire responses was highly correlated with their cognitive learning scores; and that the time searchers spent per document while searching was also highly and consistently correlated with higher-level cognitive learning scores. We also found that of the three query interaction conditions we applied, an intrinsically diverse presentation of results was associated with the highest percentage of users achieving combined factual and conceptual knowledge gains. Our study provides deeper insight into which aspects of search interaction are most effective for supporting superior learning outcomes, and the difficult problem of how learning may be assessed effectively during Web search.