Improving the Task Understanding for Knowledge Creation in Assessments

Students in higher education environments typically work with information discovered to gain and create knowledge required for their assessments and other course work. Much of this research happens online now as a required activity for students at all education levels and settings. When students access study material via the Internet and without the presence of an instructor, they may face difficulties in understanding the complete purpose of an assessment. Such difficulties can lead to poor information search practices resulting in poor creativity and low knowledge gain, which may in turn lead to mediocre information synthesis and low levels of knowledge creation. To investigate and possibly address this problem, the current study examines the information search practices of 10 novice and 5 experienced university students as they seek to gain and create knowledge for authentic assessment tasks. An additional five students received an intervention in the form of a word cloud application, which was introduced to create a visual and textual task understanding support scaffolding for the students. Results revealed evidence of knowledge gain and creation, especially by most experienced students when they used mental models to derive their searching process, and when they used notes to convert the implicit knowledge into an explicit form. The word cloud intervention additionally provided experienced students relevant learning cues from the task to improve their knowledge gain and creation, and helped improve their engagement with the online assessments tasks.

Towards Task Understanding in Visual Settings

We consider the problem of understanding real world tasks depicted in visual images. While most existing image captioning methods excel in producing natural language descriptions of visual scenes involving human tasks, there is often the need for an understanding of the exact task being undertaken rather than a literal description of the scene. We leverage insights from real world task understanding systems, and propose a framework composed of convolutional neural networks, and an external hierarchical task ontology to produce task descriptions from input images. Detailed experiments highlight the efficacy of the extracted descriptions, which could potentially find their way in many applications, including image alt text generation.

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

The ability of students to problem solve begins with interpreting the problem. When they interpret the problem inaccurately, they will likely use ineffective strategies or fail to solve the problem. Studies reported students are often incapable of identifying and articulating the problem goal, requirements/constraints, and expected output. In other words, students lack self-regulation skills, especially related to task understanding. In this study, two male and two female senior computer science students from Utah State University, USA, were recruited as research participants to learn more about their task understanding skills while engage in programming tasks. The participants were asked to answer five programming problems while thinking aloud, and their responses were video- and audio-recorded. This report focuses on one of the problems, which was a variant of the Josephus problem. Three research questions were used to guide the analysis: (a) what were the participants’ initial task understanding; (b) how did it change during the problem-solving endeavor; and (c) why did it change. All participants identified the problem goal inaccurately and as a result, selected ineffective problem-solving strategies. The analysis suggested their inaccurate task interpretations were caused by their confidence bias (i.e., a systematic cognitive error), in which they drew knowledge and strategies from irrelevant experience. Out of four participants, only one was able to defeat the confidence bias and acquired an accurate task understanding; the influencing factors and possible interventions to overcome confidence bias are discussed.