Bennett Kankuzi

ABSTRACT Earlier research on spreadsheet authors' mental models has shown that people possess several types of information about spreadsheets. Especially, when explaining a spreadsheet, the real-world and domain mental models are prominent and the spreadsheet model is suppressed, but when locating and fixing an error, one must constantly switch back and forth between the domain model and the spreadsheet model, which requires frequent use of the mapping between problem domain concepts and their spreadsheet model counterparts. This paper introduces a new spreadsheet visualization tool and its empirical evaluation. The tool translates traditional spreadsheet formulas into problem domain narratives and highlights referenced cells. The tool was found to be easy to learn and helped the participants to locate more errors in spreadsheets. Furthermore, the tool increased the use of the domain mental model and appeared to improve the mapping between the spreadsheet model and the domain model.

ABSTRACT Earlier research on spreadsheet authors' mental models has shown that people possess several types of information about spreadsheets. The research results also suggested that a tool intended to aid in comprehension and debugging of spreadsheets should make prominent real-world and problem domain concepts and map them easily to spreadsheet-specific details. This poster introduces a new spreadsheet visualization tool which was developed to demonstrate that it is possible to have an easy-to-use spreadsheet understanding and debugging tool that relieves users from spreadsheet details and lets them utilize more of their mental model of the application domain. The tool translates traditional spreadsheet formulas into problem domain narratives and highlights referenced cells.

ABSTRACT In this paper, we report on an empirical study exploring the nature of mental models of spreadsheet authors when they are explaining and debugging their own spreadsheets. Study participants were first asked to explain one of their own spreadsheets, and then to find and fix seeded errors in the same spreadsheets. Talk-aloud protocols were collected and analyzed to reveal the nature of participants' mental models in these activities. The findings indicate that the authors explain their spreadsheets mainly in terms of real-world and problem domain concepts; in debugging, they constantly switch between problem domain concepts and spreadsheet-specific concepts, although they mainly use spreadsheet-specific concepts to fix an identified error. These study findings provide insights on the need for developing spreadsheet authoring and debugging tools that correspond to spreadsheet authors' mental models of spreadsheets.

Most errors in spreadsheets are formula-based. Referenced cells in formulas are normally presented using the traditional A1 cell referencing style. A spreadsheet user has to therefore mentally map referenced
cells to their corresponding labels in order to comprehend a formula in the context of the problem domain. In this paper, we give a detailed description of an algorithm that can be used to dynamically translate traditional spreadsheet formulas to their problem domain equivalents which are easier to understand. The translation is done as one accesses a formula cell in a spreadsheet. The formula translation is based on inferred labels of referenced cells in the formula. The aim of the translation is to ease the cognitive load on the spreadsheet user and hence improving the error-prone spreadsheet development process. The paper also highlights some factors that need to be taken into consideration when dynamically translating spreadsheet formulas. The number of referenced cells in a formula and the distance of labels from referenced cells will determine the speed of the translation process hence affecting system responsiveness as one navigates through a spreadsheet. Unpredictable spatial arrangement of data in spreadsheets (spreadsheet layout) can also pose a challenge to the translation algorithm which may lead to mis-translation of spreadsheet formulas. These challenges might increase the cognitive load on the spreadsheet user hence negating the purpose of dynamically translating spreadsheet formulas.

In this paper, we explore the potential of introducing fundamental concepts of programming to first year students using spreadsheets. Many students find programming to be difficult to learn as the concepts are new and unfamiliar. On the other hand, spreadsheets have a relatively easy to use interface that provides instant viewing of results of data manipulation. Fundamental concepts of programming can then be indirectly introduced using their spreadsheet equivalents before the students start their formal introductory programming class. This paper specifically focusses on how fundamental programming concepts can be mapped to their spreadsheet equivalents.

To address the problem of errors in spreadsheets, we have investigated spreadsheet authors׳ mental models in a hope of finding cognition-based principles for spreadsheet visualization and debugging tools. To this end, we have conducted three empirical studies. The first study explored the nature of mental models of spreadsheet authors during explaining and debugging tasks. It was found that several mental models about spreadsheets are activated in spreadsheet authors׳ minds. Particularly, when explaining a spreadsheet, the real-world and domain mental models are prominent, and the spreadsheet model is suppressed; however, when locating and fixing an error, one must constantly switch back and forth between the domain model and the spreadsheet model, which requires frequent use of the mapping between problem domain concepts and their spreadsheet model counterparts. The second study examined the effects of replacing traditional spreadsheet formulas with problem domain narratives in the context of a debugging task. Domain narratives were found to be easy to learn and they helped participants to locate more errors in spreadsheets. Furthermore, domain narratives also increased the use of the domain mental model and appeared to improve the mapping between the domain and spreadsheet models. The third study investigated the effects of allowing spreadsheet authors to fix errors by editing domain narratives, thus relieving them from the use of traditional low-level cell references. This scenario was found to promote spreadsheet authors using even more of their domain mental model in a manner that completely overshadowed the use of their spreadsheet mental model. Thus, from a mental model perspective, it is possible to devise a new spreadsheet paradigm that uses domain narratives in place of traditional spreadsheet formulas, thus automatically presenting spreadsheet content so that it prompts spreadsheet authors to think in a manner that closely corresponds to their mental models of the application domain.

This thesis tackled the problem of errors in spreadsheets by studying mental models of spreadsheet authors performing various spreadsheet activities. Using a constructive research approach, we conducted three empirical studies and developed two spreadsheet visualization tools. Our results show the need for spreadsheet tools that reflect spreadsheet authors’ mental models, and for a paradigm shift which allows spreadsheets to be developed in terms of a mental model of the application domain.

Earlier research on spreadsheet authors' mental models has shown that people possess several types of information about spreadsheets. The research results also suggested that a tool intended to aid in comprehension and debugging of spreadsheets should make prominent real-world and problem domain concepts and map them easily to spreadsheet-specific details. This poster introduces a new spreadsheet visualization tool which was developed to demonstrate that it is possible to have an easy-to-use spreadsheet understanding and debugging tool that relieves users from spreadsheet details and lets them utilize more of their mental model of the application domain. The tool translates traditional spreadsheet formulas into problem domain narratives and highlights referenced cells.

Earlier research on spreadsheet authors' mental models has shown that people possess several types of information about spreadsheets. Especially, when explaining a spreadsheet, the real-world and domain mental models are prominent and the spreadsheet model is suppressed, but when locating and fixing an error, one must constantly switch back and forth between the domain model and the spreadsheet model, which requires frequent use of the mapping between problem domain concepts and their spreadsheet model counterparts. This paper introduces a new spreadsheet visualization tool and its empirical evaluation. The tool translates traditional spreadsheet formulas into problem domain narratives and highlights referenced cells. The tool was found to be easy to learn and helped the participants to locate more errors in spreadsheets. Furthermore, the tool increased the use of the domain mental model and appeared to improve the mapping between the spreadsheet model and the domain model.

In this paper, we report on an empirical study exploring the nature of mental models of spreadsheet authors when they are explaining and debugging their own spreadsheets. Study participants were first asked to explain one of their own spreadsheets, and then to find and fix seeded errors in the same spreadsheets. Talk-aloud protocols were collected and analyzed to reveal the nature of participants' mental models in these activities. The findings indicate that the authors explain their spreadsheets mainly in terms of real-world and problem domain concepts; in debugging, they constantly switch between problem domain concepts and spreadsheet-specific concepts, although they mainly use spreadsheet-specific concepts to fix an identified error. These study findings provide insights on the need for developing spreadsheet authoring and debugging tools that correspond to spreadsheet authors' mental models of spreadsheets.