Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

The dynamics of learner participation in a virtual learning environment

While online students should take charge of their own learning and form collaborative learning communities, constructivist instructors should scaffold online learning without dominating course discussions. This research continues the longitudinal investigation of web-based courses at the Faculty of Education, University of Pretoria. The mixed methodological approach this investigation followed consisted predominantly of qualitative methods, augmented with quantitative approaches. I used two distinct online tools to explore student participation in an eight-week online Masters’-level course delivered via the WebCT™ platform. First, I reviewed the use of metaphors in the literature by a framework of requirements for successful online learning. The use of metaphor supports constructivism, facilitates course interaction, helps to avoid students’ initial inertia in online discussions, and contributes to the development of virtual learning communities. I researched how an explanatory metaphor as tool supported online participation and indicated that metaphors eased students’ communication of important and difficult issues. Secondly, I used the tool of a covert virtual student that also acted as an additional facilitator and course helper. I examined the ethical implications of the carefully concealed real identity of the mythical online helper, methical Jane. As she took part in all course activities and assignments, as well as providing her co-students with cognitive and technical support, the students accepted and integrated her presence in their virtual learning community. I consequently analysed students’ reactions to her identity after disclosure of her origin after the course. Although the exposure precipitated students’ shock, disbelief and dismay as she was a convincing virtual student, they did not object to the presence of a virtual student, but rather felt betrayed due to her hidden real identity. The benefits of this teaching intervention include experts supplying technical expertise, multiple faculty enriching the learning experience, and support and teaching assistants and tutors participating with smaller groups in large online classes. I further examined how frequency of course access, discussion postings, collaborative behaviour and integration into a virtual learning community relate to learning and course completion. Quantitative indices indicated highly significant differences between the stratifications of student performance. Absent and seldom-contributing students risked missing the benefits of the online learning community. Students were discontent with peers who rarely and insufficiently contributed to group assignments. Low participation varied from only reading, skimming, or deliberately harvesting others’ contributions, to high student contributions of little value. Conclusions on the formation of an online learning community indicate that the passport to membership of the community is quality participation, rather than prior peer acquaintance. I indicated that students’ learning benefited from contributing high quality inputs to online learning communities while students with poor participation did not benefit from the online learning community. Online facilitators contribute to students’ learning through the timeliness and quality of tailored scaffolding. Recommendations for future research include uncovering the reasons for students’ stressful experiences of online learning; the effect of online assessment on student course participation; the alignment of learning metaphors in multi-cultural learning environments; and the support of non-participating online students.Thesis (PHD)--University of Pretoria, 2009.Curriculum Studiesunrestricte

An influence model of the experience of learning programming

Learning to program is difficult for many students all over the world with programming courses often experiencing high failure and attrition rates. The teaching of programming is still considered a major challenge by educators. At the same time, programming is becoming a key skill required not only of IT graduates but also of students in other disciplines and is becoming more important to a wider range of people. Today’s university students also practice their learning in an extended learning environment that extends well beyond the classroom. There has been considerable research into the teaching of programming in the computing education field, with many studies focussing on content and delivery. More recently, researchers have recognised the need for a greater understanding of how students experience learning to program, from the student’s perspective. This study contributes to this growing body of knowledge by exploring, in depth, the wide range of influences on the student learning experience of programming. A qualitative study was conducted that interviewed 31 Information Systems students about their experiences in learning programming. The interview transcripts were analysed using a Grounded Theory methodology. A new theory of the Influences on the Student Learning Experience of Programming was developed from the analysis, which is more holistic and comprehensive than previous theories. The learning experience of programming involves a complex interaction of a wide range of influences. A major influence is the student’s Perceived Personal Relevance towards programming. Students who perceive that programming is relevant to their future career goals are far more motivated to learn it. Perceived Personal Relevance, together with Learning Trait and Skill Level describe the Learner Nature of the student, which influences their Learning Behaviours. The influences within Learning Behaviours include Core Learning Perspectives (Ownership of learning, Learning Task Intent and Problem solving Behaviours), Patterns of Collaboration and Patterns of Information Use. Patterns of Collaboration describe how students interact with and use their Personal Networks, and include four levels of dependency: One Way Dependent, Two Way Co-Dependent, Collaborative Independent and Solitary Independent. Patterns of Information Use describe the different ways students interact with and use their information sources. The theory includes Programming Learner Profiles, which encapsulate the relationships and influences between Learner Nature and Learning Behaviours. Each profile describes, in essence, the nature and behaviour of different types of students. Seven distinct Programming Learner Profiles were identified in the study: Reluctant Beginner, Willing Beginner, Keen Beginner, Budding Manager, Budding Practitioner, Budding Developer and Advanced Developer. This new theory gives educators a greater insight into what students are thinking and doing when learning to program and potential strategies that can improve learning outcomes