Volunteer organizations face two challenges not found in non-volunteer organizations: recruiting and retaining volunteers. While social media use is increasing amongst individuals, its use and effectiveness for volunteer recruitment and retention by volunteer organizations is unknown. The dissertation reports the results of three studies to investigate this important question. Using a mixed-methods approach, it addressed the dual nature of social media and its effectiveness by including volunteer organizations and social media users. This dissertation found that although volunteer organizations are not using social media effectively, they could virtualize requirements of the recruitment process by focusing on relatable events instead of sterile marketing. This dissertation contributes to information systems literature by extending the post-acceptance model of IS continuance to show that social media mediates volunteer continuance. It further contributes by revealing a potential new area of research, i.e., the use and effectiveness of online social media for volunteer organizations.

This thesis is about diagrammatic reasoning and error-finding in conceptual modeling diagrams. Specifically, the differences of the cognitive strategies and behaviors of notation-familiar participants versus domain-familiar participants working on conceptual modeling diagrams are inspected. The domain-familiar participants are experienced in the topic being represented, but they do not have any formal training in software development representations. On the other hand, the notation-familiar participants are educated in software representations, but unfamiliar with the topic represented. The main experiment and the follow-up experiment also aim to study how some properties of diagrams affect the error-finding behaviors. The participant groups&rsquo / performances in the main experiment are investigated and compared by the analysis of verbal protocol data and eye movement data. The combination of the two different methods enhances detailed analyses. In the follow-up experiment, only eye movement data is involved to evaluate how some properties of diagrams affect problem-solving. By means of both experiments, it is concluded that diagrammatic complexity has a negative effect on reasoning whereas the degree of causal chaining improves diagrammatic reasoning. In the main experiment, some differences in the diagrammatic reasoning processes between the groups are observed, too. The notation-familiar participants are observed to be more successful in error-finding although they are unfamiliar with the topic. This study underlines the interaction of cognitive science and software engineering by integrating eye movement data, verbal protocol analysis and performance data into the cognitive inspection of software engineering notations.

The current study compared participants’ ability to perform tasks using two nutrition labels: a control Nutrition Facts table of the current Canadian format (n=64), and an experimental label (n=64), identical to the control label with the exception of a footnote explaining how to interpret percent daily values. A 25% subset of participants answered questions using a think aloud technique, and data was analyzed using content analysis. The main outcome measured was ability to interpret percentages correctly, with ability to compare, define and manipulate information as secondary outcomes. No significant differences were seen in ability to perform tasks between the experimental and control conditions for any outcomes. As determined by chi square tests, higher performance was associated with higher education, being male, and report of previous Nutrition Facts table use. Verbal protocol analysis identified that interpretation of percentages was based on the meal, food type, and comparison to other foods.

One of the key determinants of an organization’s success is its ability to adapt to marketplace change. Given this reality, how do organizations survive or even thrive in today’s dynamic markets? The answer to this question is highly related to the adaptability of one of the organization’s key resource: its employees. Indeed, the central component of an organization’s success will depend on its ability to drive changes in the mental models of individual employees. Moreover, a critical facilitator of that will be the development of decision support tools that support change of those mental models. In response to this need there has been a tremendous growth in business analytic decision support tools, estimated to reach almost $200 billion in sales by 2019. The premise of this research is that these decision support tools are ill-suited to support true mental model change because they have focused on a feedback-enabled view and generally lack a predictive (feedforward-enabled) view of the likely outcomes of the decision. The purpose of this research is to study how changes in mental models can be facilitated through this feedforward mechanisms within the DSS tool. This research used a mixed method approach, leveraging the strengths of quantitative and qualitative research methodologies, to study this research question. The research showed that the feedforward-enabled DSS tool did create more mental model change and alignment (versus an ideal solution) compared to the control. The feedforward enabled tool also produced better alignment than the feedback-enabled decision support tool. In fact, the feedback-enabled decision support was shown to result in a poorer alignment with the ideal solution. This paper concludes by suggesting five areas for future research. / Business Administration/Management Information Systems

abstract: Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective. / Dissertation/Thesis / M.S. Computer Science 2011

abstract: Assemblers and compilers provide feedback to a programmer in the form of error messages. These error messages become input to the debugging model of the programmer. For the programmer to fix an error, they should first locate the error in the program, understand what is causing that error, and finally resolve that error. Error messages play an important role in all three stages of fixing of errors. This thesis studies the effects of error messages in the context of teaching programming. Given an error message, this work investigates how it effects student’s way of 1) understanding the error, and 2) fixing the error. As part of the study, three error message types were developed – Default, Link and Example, to better understand the effects of error messages. The Default type provides an assembler-centric single line error message, the Link type provides a program-centric detailed error description with a hyperlink for more information, and the Example type provides a program centric detailed error description with a relevant example. All these error message types were developed for assembly language programming. A think aloud programming exercise was conducted as part of the study to capture the student programmer’s knowledge model. Different codes were developed to analyze the data collected as part of think aloud exercise. After transcribing, coding, and analyzing the data, it was found that the Link type of error message helped to fix the error in less time and with fewer steps. Among the three types, the Link type of error message also resulted in a significantly higher ratio of correct to incorrect steps taken by the programmer to fix the error. / Dissertation/Thesis / Masters Thesis Software Engineering 2017