Pub Date : 1996-04-01DOI: 10.1080/10447319609526146
Jennifer D. E. Thomas
In the Information Systems community, the term ease of use has taken on many meanings and interpretations, and various factors said to contribute to it have been investigated. To determine whether studies in this area have focused on the right aspects, or need to be refocused, this study sought to ascertain whether some level of agreement exists among experienced users regarding the importance of various package design and assistance features for ease of use and the support they offer to identified learning dimensions. The results point to some degree of agreement among these users on the importance of individual features for ease of use, though they did not agree on all features. The panel agreed that the identified learning dimensions should be equally supported by package features. There was also reasonable agreement that certain features support certain learning dimensions. The results point to a need to refocus some of the research areas which have been considered important for ease of use and sugges...
{"title":"The importance of package features and learning factors for ease of use","authors":"Jennifer D. E. Thomas","doi":"10.1080/10447319609526146","DOIUrl":"https://doi.org/10.1080/10447319609526146","url":null,"abstract":"In the Information Systems community, the term ease of use has taken on many meanings and interpretations, and various factors said to contribute to it have been investigated. To determine whether studies in this area have focused on the right aspects, or need to be refocused, this study sought to ascertain whether some level of agreement exists among experienced users regarding the importance of various package design and assistance features for ease of use and the support they offer to identified learning dimensions. The results point to some degree of agreement among these users on the importance of individual features for ease of use, though they did not agree on all features. The panel agreed that the identified learning dimensions should be equally supported by package features. There was also reasonable agreement that certain features support certain learning dimensions. The results point to a need to refocus some of the research areas which have been considered important for ease of use and sugges...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133795212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-04-01DOI: 10.1080/10447319609526145
C. Ntuen
The flexibility and usability of graphic‐based HCIs can be increased by adding a natural language interface with command menus. Among the several other advantages, such embellishment offers the user an opportunity for direct expression of his or her behaviors, goals, intentions, and objectives along the continuum of the task knowledge. The existing graphic‐based HCIs that operate on active symbologies and icons assume the user's mental models to correlate with perceptual and cognitive levels of the task understanding. This obviously increases mental loads and the frustration of the human adapting to the system. In reality, the system should be designed to adapt to the user's behavior and skill level. In order to improve the current design of graphic‐based HCIs, we have formulated theories of command production language that will enhance the user's ability to interact with the system. The methods developed combine the theory of expert database with formal grammar to develop command‐production rules using a...
{"title":"A theory of command language dialogue for a knowledge-based human-computer interaction","authors":"C. Ntuen","doi":"10.1080/10447319609526145","DOIUrl":"https://doi.org/10.1080/10447319609526145","url":null,"abstract":"The flexibility and usability of graphic‐based HCIs can be increased by adding a natural language interface with command menus. Among the several other advantages, such embellishment offers the user an opportunity for direct expression of his or her behaviors, goals, intentions, and objectives along the continuum of the task knowledge. The existing graphic‐based HCIs that operate on active symbologies and icons assume the user's mental models to correlate with perceptual and cognitive levels of the task understanding. This obviously increases mental loads and the frustration of the human adapting to the system. In reality, the system should be designed to adapt to the user's behavior and skill level. In order to improve the current design of graphic‐based HCIs, we have formulated theories of command production language that will enhance the user's ability to interact with the system. The methods developed combine the theory of expert database with formal grammar to develop command‐production rules using a...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132241805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-04-01DOI: 10.1080/10447319609526147
Jill Gerhardt-Powals
Many computer systems today are not satisfactory to their users. Often the user interface does not receive the attention that it deserves, even though to the user, the interface is the most important part of the computer system. Further, many interfaces are not designed with reference to how humans process information. This research addressed this problem by designing and evaluating a cognitively engineered interface. Cognitive engineering of a human‐computer interface is the leveraging of empirical findings from the cognitive sciences and application of those findings to the design of the interface. It was hypothesized that a cognitively engineered interface is superior to interfaces that are not cognitively engineered. Ten cognitive‐design principles were extracted from the literature and explicitly applied to the design of an interface. Reaction time, accuracy, workload, and preference for this interface were experimentally determined and compared with that of two other interfaces. The other two interf...
{"title":"Cognitive engineering principles for enhancing human-computer performance","authors":"Jill Gerhardt-Powals","doi":"10.1080/10447319609526147","DOIUrl":"https://doi.org/10.1080/10447319609526147","url":null,"abstract":"Many computer systems today are not satisfactory to their users. Often the user interface does not receive the attention that it deserves, even though to the user, the interface is the most important part of the computer system. Further, many interfaces are not designed with reference to how humans process information. This research addressed this problem by designing and evaluating a cognitively engineered interface. Cognitive engineering of a human‐computer interface is the leveraging of empirical findings from the cognitive sciences and application of those findings to the design of the interface. It was hypothesized that a cognitively engineered interface is superior to interfaces that are not cognitively engineered. Ten cognitive‐design principles were extracted from the literature and explicitly applied to the design of an interface. Reaction time, accuracy, workload, and preference for this interface were experimentally determined and compared with that of two other interfaces. The other two interf...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125590323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-04-01DOI: 10.1080/10447319609526144
H. Bullinger, K. Fähnrich, A. Weisbecker
GENIUS (GENerator for user Interfaces Using Software‐ergonomic rules) comprises a method and the supporting tool environment for the generation of user interfaces from extended data models by means of software‐ergonomic rules. The representation of the user interface is based on views defined for the data model. The basic dialogue structure is derived from the data model structure. This ensures the development of task‐appropriate user interfaces by transferring the characteristics of the application domain and the user's tasks reflected in the data model to the dialogue structure. The automatic generation of the user interface from the defined views is carried out by a rule‐based system with explicit design rules derived from existing guidelines. Output is generated for an existing user interface management system. The software‐ergonomic rules in the generation process guarantee the consistent use of interaction objects and a uniformed dialogue structure. The use of the data model as the starting point fo...
{"title":"GENIUS: Generating software-ergonomic user interfaces","authors":"H. Bullinger, K. Fähnrich, A. Weisbecker","doi":"10.1080/10447319609526144","DOIUrl":"https://doi.org/10.1080/10447319609526144","url":null,"abstract":"GENIUS (GENerator for user Interfaces Using Software‐ergonomic rules) comprises a method and the supporting tool environment for the generation of user interfaces from extended data models by means of software‐ergonomic rules. The representation of the user interface is based on views defined for the data model. The basic dialogue structure is derived from the data model structure. This ensures the development of task‐appropriate user interfaces by transferring the characteristics of the application domain and the user's tasks reflected in the data model to the dialogue structure. The automatic generation of the user interface from the defined views is carried out by a rule‐based system with explicit design rules derived from existing guidelines. Output is generated for an existing user interface management system. The software‐ergonomic rules in the generation process guarantee the consistent use of interaction objects and a uniformed dialogue structure. The use of the data model as the starting point fo...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132346056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-12-31DOI: 10.1080/10447319609526151
K. Yoshida, H. Motoda
The analysis of the user behavior is one important function of the intelligent user interface because, by analyzing the user behavior, it becomes possible to understand the user intention and release the user from tedious tasks which are often required to use a fast but low-level interface. The acquisition of the user behavior model is crucial. Most studies meant to realize an intelligent interface system only analyze superficial user behaviors, from which to automate the repetitions. Their user models tend to be simple and do not reproduce the behavior well enough. This paper presents a new framework that analyzes the computational processes activated by the user commands to build the user behavior model. An important feature of the proposed framework is the analysis of data dependency between the user commands. A user-adaptive interface system, CHpBoard, was developed to show the adequacy of this framework. It analyzes the I/O relationship between applications in the past task history, selects the next application, and creates scripts which enable complex task execution by a single command.
{"title":"Automated user modeling for intelligent interface","authors":"K. Yoshida, H. Motoda","doi":"10.1080/10447319609526151","DOIUrl":"https://doi.org/10.1080/10447319609526151","url":null,"abstract":"The analysis of the user behavior is one important function of the intelligent user interface because, by analyzing the user behavior, it becomes possible to understand the user intention and release the user from tedious tasks which are often required to use a fast but low-level interface. The acquisition of the user behavior model is crucial. Most studies meant to realize an intelligent interface system only analyze superficial user behaviors, from which to automate the repetitions. Their user models tend to be simple and do not reproduce the behavior well enough. This paper presents a new framework that analyzes the computational processes activated by the user commands to build the user behavior model. An important feature of the proposed framework is the analysis of data dependency between the user commands. A user-adaptive interface system, CHpBoard, was developed to show the adequacy of this framework. It analyzes the I/O relationship between applications in the past task history, selects the next application, and creates scripts which enable complex task execution by a single command.","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126115523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-10-01DOI: 10.1080/10447319509526130
S. Sebillotte
This article presents the precepts of a methodology for extracting characteristics relevant for human‐computer interface design. This methodology takes into account the compatibility of computer interfaces with human operators’ tasks. The article uses examples to illustrate the logical progression of the proposed approach. Methods for gathering data about human operators’ tasks (interview, trace analysis, experimental simulation) and a task description according to the methode analytique de description or analytic description method (MAD) formalism are outlined. The interface specification is described using an example to show how some important characteristics for human‐computer interfaces may be extracted from the task description. Such characteristics need to be considered in order for the interface to satisfy the ergonomie criteria.
{"title":"Methodology guide to task analysis with the goal of extracting relevant characteristics for human-computer interfaces","authors":"S. Sebillotte","doi":"10.1080/10447319509526130","DOIUrl":"https://doi.org/10.1080/10447319509526130","url":null,"abstract":"This article presents the precepts of a methodology for extracting characteristics relevant for human‐computer interface design. This methodology takes into account the compatibility of computer interfaces with human operators’ tasks. The article uses examples to illustrate the logical progression of the proposed approach. Methods for gathering data about human operators’ tasks (interview, trace analysis, experimental simulation) and a task description according to the methode analytique de description or analytic description method (MAD) formalism are outlined. The interface specification is described using an example to show how some important characteristics for human‐computer interfaces may be extracted from the task description. Such characteristics need to be considered in order for the interface to satisfy the ergonomie criteria.","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-10-01DOI: 10.1080/10447319509526132
A. Mitrovic, S. Djordjevic-Kajan
Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...
{"title":"Interactive reconstructive student modeling: A machine-learning approach","authors":"A. Mitrovic, S. Djordjevic-Kajan","doi":"10.1080/10447319509526132","DOIUrl":"https://doi.org/10.1080/10447319509526132","url":null,"abstract":"Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-10-01DOI: 10.1080/10447319509526131
Qing Gong, G. Salvendy
Computer users vary greatly in their abilities to use a software interface efficiently. One factor that apparently affects users’ efficiency in using an interface is the changes in their skill levels. In this study, an adaptive interface (of menu and command) is presented that dynamically adjusts to users’ changing skill levels. The mechanism of an adaptive interface is described and discussed. The validity and usability of the adaptive interface is tested with 40 participants in an experiment that used a between‐subject experimental design for interface style. The independent variables were interface style (menu, command, hybrid, and adaptive) and skill level (starting session and ending sessions). The dependent variables were task completion time, number of steps used, ratio (of using menu mode over menu and command modes), perceived memory load, and satisfaction with the interface styles. The task‐completion time and ratio data indicate that the adaptive interface produced significantly better performa...
{"title":"An approach to the design of a skill adaptive interface","authors":"Qing Gong, G. Salvendy","doi":"10.1080/10447319509526131","DOIUrl":"https://doi.org/10.1080/10447319509526131","url":null,"abstract":"Computer users vary greatly in their abilities to use a software interface efficiently. One factor that apparently affects users’ efficiency in using an interface is the changes in their skill levels. In this study, an adaptive interface (of menu and command) is presented that dynamically adjusts to users’ changing skill levels. The mechanism of an adaptive interface is described and discussed. The validity and usability of the adaptive interface is tested with 40 participants in an experiment that used a between‐subject experimental design for interface style. The independent variables were interface style (menu, command, hybrid, and adaptive) and skill level (starting session and ending sessions). The dependent variables were task completion time, number of steps used, ratio (of using menu mode over menu and command modes), perceived memory load, and satisfaction with the interface styles. The task‐completion time and ratio data indicate that the adaptive interface produced significantly better performa...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-10-01DOI: 10.1080/10447319509526129
H. Mori, Y. Hayashi
This study investigated the potential visual interference imposed by displayed peripheral windows that are not central to a user's current task performance. In particular, the study examined the relation between foveal vision and peripheral vision activities in multiwindow systems. It was suggested that the number and layout of the windows in a multiwindow system can interfere with a user's activities while performing a task. Results from a visual search experiment were indicated as follows: Displayed peripheral windows interfered with a user's current task performance. The number of the peripheral windows is a significant factor in the interference. The types of the layout, overlapping or nonoverlapping, are also a significant factor in the interference. The activities of the foveal vision get worse when the visual position of the task performance is closer to the peripheral windows. These factors have different influences depending on whether the peripheral windows are static or dynamic We discuss these...
{"title":"Visual interference with users' tasks on multiwindow systems","authors":"H. Mori, Y. Hayashi","doi":"10.1080/10447319509526129","DOIUrl":"https://doi.org/10.1080/10447319509526129","url":null,"abstract":"This study investigated the potential visual interference imposed by displayed peripheral windows that are not central to a user's current task performance. In particular, the study examined the relation between foveal vision and peripheral vision activities in multiwindow systems. It was suggested that the number and layout of the windows in a multiwindow system can interfere with a user's activities while performing a task. Results from a visual search experiment were indicated as follows: Displayed peripheral windows interfered with a user's current task performance. The number of the peripheral windows is a significant factor in the interference. The types of the layout, overlapping or nonoverlapping, are also a significant factor in the interference. The activities of the foveal vision get worse when the visual position of the task performance is closer to the peripheral windows. These factors have different influences depending on whether the peripheral windows are static or dynamic We discuss these...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134404304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-10-01DOI: 10.1080/10447319509526127
M. Lehto, Wenli Zhu, Bryan Carpenter
A series of two experiments was conducted. In Experiment 1, participant performance when using a hypertext electronic reference system was compared to using a conventional reference book. The links in this hypertext were based on the index entries in the corresponding 529‐page book. Specific topics and particular facts were located much faster and more accurately using the hypertext system than for the book. These advantages increased when participants searched for information that was either not included or referred to indirectly in the index. However, hypertext did not have an advantage over text on learning tasks. The conclusion was that hypertext is superior to text only for “reading‐to‐do” tasks similar to those a designer may perform when consulting a reference book. Experiment 2 compared user performance when the links corresponded exactly to the original index of a 545‐page textbook on ergonomics to performance when the links were generated by computer key‐word searches. Strong advantages were fou...
{"title":"The relative effectiveness of hypertext and text","authors":"M. Lehto, Wenli Zhu, Bryan Carpenter","doi":"10.1080/10447319509526127","DOIUrl":"https://doi.org/10.1080/10447319509526127","url":null,"abstract":"A series of two experiments was conducted. In Experiment 1, participant performance when using a hypertext electronic reference system was compared to using a conventional reference book. The links in this hypertext were based on the index entries in the corresponding 529‐page book. Specific topics and particular facts were located much faster and more accurately using the hypertext system than for the book. These advantages increased when participants searched for information that was either not included or referred to indirectly in the index. However, hypertext did not have an advantage over text on learning tasks. The conclusion was that hypertext is superior to text only for “reading‐to‐do” tasks similar to those a designer may perform when consulting a reference book. Experiment 2 compared user performance when the links corresponded exactly to the original index of a 545‐page textbook on ergonomics to performance when the links were generated by computer key‐word searches. Strong advantages were fou...","PeriodicalId":208962,"journal":{"name":"Int. J. Hum. Comput. Interact.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126479324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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