Recommendation systems play an important role in today's digital world. They have found applications in various areas such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system to recommend daily exercise activities to users based on their history, profiles and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learner calculates the uncertainty of the recommendation system at each time step for each user and asks an expert for recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance, and use it to determine when to ask experts for feedback. Our experimental results on a mHealth and MovieLens datasets show improved accuracy after incorporating the real-time active learner with the recommendation system.

We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of these interventions on achieving behavior-change goals. Our approach in the Fittle+ systems rests on the idea that to master the complex fabric of a new healthy lifestyle, one must weave together a new set of healthy habits that over-ride the old unhealthy habits. To achieve these aims, we have developed a series of mHealth platforms that provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that provide additional support to the acquisition and maintenance of healthy habits. We present experimental evidence collected so far for statistically significant improvements in behavior change in eating, exercise, and physical activity for the following scaffolding interventions: guided mastery, teaming, self-affirmation, and implementation intentions. We also present predictive computational ACT-R models of daily individual behavior goal success for data collected in guided mastery and implementation intention studies that address goal-striving and habit formation mechanisms.

Consumers are increasingly using the Internet to find information to support health-related decisions. This study evaluated two software tools, Mr. Taggy and SparTag.us, which are designed to help users filter and identify relevant information and organize and make sense of information. Participants included 80 adults, aged 35–82, who performed health information search tasks. Twenty participants used Mr. Taggy and another 20 used Spartag.us. The search tasks varied according to the tool characteristics. Two corresponding control groups, each with 20 participants, performed the tasks using the Google search engine. Participants also completed cognitive ability tests and a usability questionnaire. The findings indicated that participants using the tools found them useful and usable. However, participants using Google had significantly better task performance. Although cognitive abilities were related to task performance, a wider variety of abilities were related to performance in the aided conditions compared with the unaided conditions. The findings underscore the importance of ensuring that the cognitive demands associated with software tools do not outweigh the benefits of these tools, especially for users who are not highly practiced at using them.

Automated coaching systems offer a convenient, cost-effective way to reduce stress, which can be a serious health issue. However, one concern with such systems is compliance; users fail to achieve daily stress reduction goals because goals are too easy or too difficult. To address this, we built DStress (Design for Stress), a theoretically grounded system that sets adaptive goals in three coaching dimensions: Exercise, Meditation and Accessibility. DStress modifies goal-difficulty based on the individual's immediately previous performance. In a 28-day deployment with 65 users, DStress reduced scores on one direct measure of stress almost in half, significantly more than two other non-adaptive coaching strategies. However, on a second direct stress measure, no improvement was found. There were also no improvements on other indirect stress measures. Analysis of 2842 user-generated reports suggests our findings were the result of DStress balancing compliance against the degree of challenge of the goals it would set.

Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sources that hold viewpoints similar to their own, a phenomenon known as confirmation bias. This suggests that when learning about a controversial topic, human biases and viewpoints about the topic may affect what is considered “trustworthy” or credible. It is an interesting challenge to build systems that can help users overcome this bias and help them decide the truthfulness of claims. In this article, we study various factors that enable humans to acquire additional information about controversial claims in an unbiased fashion. Specifically, we designed a user study to understand how presenting evidence with contrasting viewpoints and source expertise ratings affect how users learn from the evidence documents. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well‐rounded understanding of the topic. Furthermore, explicit knowledge of the credibility of the sources and the context in which the source provides the evidence document not only affects what users read but also whether they perceive the document to be credible.

Judging topical expertise of micro-blogger is one of the key challenges for information seekers when deciding which information sources to follow. However, it is unclear how useful different types of information are for people to make expertise judgments and to what extent their background knowledge influences their judgments. This study explored differences between experts and novices in inferring expertise of Twitter users. In three conditions, participants rated the level of expertise of users after seeing (1) only the tweets, (2) only the contextual information including short biographical and user list information, and (3) both tweets and contextual information. Results indicated that, in general, contextual information provides more useful information for making expertise judgment of Twitter users than tweets. While the addition of tweets seems to make little difference, or even add nuances to novices' expertise judgment, experts' judgments were improved when both content and contextual information were presented.

Deciding whether a claim is true or false often requires understanding the evidence supporting and contradicting the claim. However, when learning about a controversial claim, human biases and viewpoints may affect which evidence documents are considered "trustworthy" or credible. It is important to overcome this bias and know both viewpoints to get a balanced perspective. In this paper, we study various factors that affect learning about the truthfulness of controversial claims. We designed a user study to understand the impact of these factors. Specifically, we studied the impact of presenting evidence with contrasting viewpoints and source expertise rating on how users accessed the evidence documents. This would help us optimize how to teach users about controversial topics in the most effective way, and to design better claim verification systems. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well-rounded understanding of the topic. Furthermore, explicit knowledge of the source credibility and the context not only affects what users read, but also how credible they perceive the document to be.

Making sense of the world using information technology has become a ubiquitous activity in the digital era. Sensemaking, as in to make sense, suggests an active processing of information to achieve...

We developed a low-effort interaction method called Click2Tag for social bookmarking. Information foraging theory predicts that the production of tags will increase as the effort required to do so is lowered, while the amount of time invested decreases. However, models of human memory suggest that changes in the tagging process may affect subsequent human memory for the tagged material. We compared (1) low-effort tagging by mouse-clicking (Click2Tag), (2) traditional tagging by typing (type-to-tag), and (3) baseline, no tagging conditions. Our results suggest that (a) Click2Tag increases tagging rates, (b) Click2Tag improves recognition of facts from the tagged text when compared to type-to-tag, and (c) Click2Tag is comparable to the no-tagging baseline condition on recall measures. Results suggest that tagging by clicking strengthens the memory traces by repeated readings of relevant words in the text and, thus, improves recognition.

Social tagging arose out of the need to organize found content that is worth revisiting. A significant side effect has been the use of social tagging sites as navigational signposts for interesting content. The collective behavior of users who tagged contents seems to offer a good basis for exploratory search interfaces, even for users who are not using social bookmarking sites. In this paper, we present the design of a tag-based exploratory system and detail an experiment in understanding its effectiveness. The tag-based search system allows users to utilize relevance feedback on tags to indicate their interest in various topics, enabling rapid exploration of the topic space. The experiment shows that the system seems to provide a kind of scaffold for users to learn new topics.

In prior work we reported on the design of a social annotation system, SparTag.us, for use in sensemaking activities such as work-group reading and report writing. Previous studies of note-taking systems have demonstrated behavioral differences in social annotation practices, but are not clear in the actual performance gains provided by social features. This paper presents a laboratory study aimed at evaluating the learning effect of social features in SparTag.us. We found significant learning gains, and consider implications for design and for understanding the underlying mechanisms in play when people use social annotation systems.

User interfaces and information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social information foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.

An experiment was conducted to study how credibility judgments about Wikipedia are affected by providing users with an interactive visualization (WikiDashboard) of article and author editing history. Overall, users who self-reported higher use of Internet information and higher rates of Wikipedia usage tended to produce lower credibility judgments about Wikipedia articles and authors. However, use of WikiDashboard significantly increased article and author credibility judgments, with effect sizes larger than any other measured effects of background media usage and attitudes on Wikiepedia credibility. The results suggest that increased exposure to the editing/authoring histories of Wikipedia increases credibility judgments.

Tagging systems such as del.icio.us and Diigo have become important ways for users to organize information gathered from the Web. However, despite their popularity among early adopters, tagging still incurs a relatively high interaction cost for the general users. We introduce a new tagging system called SparTag.us, which uses an intuitive Click2Tag technique to provide in situ, low cost tagging of web content. SparTag.us also lets users highlight text snippets and automatically collects tagged or highlighted paragraphs into a system-created notebook, which can be later browsed and searched. We report several user studies aimed at evaluating Click2Tag and SparTag.us.

This study investigated the influences of knowledge, particularly Internet, Web browser, and search engine knowledge, as well as cognitive abilities on older adult information seeking on the Internet. The emphasis on aspects of cognition was informed by a modeling framework of search engine information-seeking behavior. Participants from two older age groups were recruited: twenty people in a younger-old group (ages 60-70) and twenty people in an older-old group (ages 71-85). Ten younger adults (ages 18-39) served as a comparison group. All participants had at least some Internet search experience. The experimental task consisted of six realistic search problems, all involving information related to health and well-being and which varied in degree of complexity. The results indicated that though necessary, Internet-related knowledge was not sufficient in explaining information-seeking performance, and suggested that a combination of both knowledge and key cognitive abilities is important for successful information seeking. In addition, the cognitive abilities that were found to be critical for task performance depended on the search problem's complexity. Also, significant differences in task performance between the younger and the two older age groups were found on complex, but not on simple problems. Overall, the results from this study have implications for instructing older adults on Internet information seeking and for the design of Web sites.

We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user’s goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information Wai-Tat Fu is an applied cognitive scientist with interests in human–computer interaction, cognitive modeling, information seeking, interactive decision making, and cognitive skill acquisition; he is an Assistant Professor in the Human Factors Division and Beckman Institute of Advanced Science and Technology at the University

We present an experiment that compares how people perform search tasks in a degree-of-interest browser and in a Windows-Explorer-like browser. Our results show that, whereas users do attend to more information in the DOI browser, they do not complete the task faster than in an Explorer-like browser. However, in both types of browser, users are faster to complete high information scent search tasks than low information scent tasks. We present an ACT-R computational model of the search task in the DOI browser. The model describes how a visual search strategy may combine with semantic aspects of processing, as captured by information scent. We also describe a way of automatically estimating information scent in an ontological hierarchy by querying a large corpus (in our case, Google's corpus).

The Hyperbolic Tree is a focus + context information visualization that has been developed to amplify users' ability to navigate large tree-structured information systems. Information scent is a theoretical construct that captures one kind of interaction between task and display. Information scent is provided by task-relevant display cues, such as node labels on a tree that influence a user's visual search behavior and navigation decisions. An empirical Accuracy of Scent (AOS) score was developed to characterize a set of tasks that required users to find (Retrieval Tasks) or compare (Comparison Tasks) information in tree structures. Two experiments investigated the effect of information scent (tasks with different AOS scores) on performance with the Hyperbolic Tree Browser and the Microsoft Windows File Browser, which is a widely available conventional browser. Experiment 1 found no overall difference in performance time between the two browsers, but did reveal a marginal interaction of information scent with browser performance on Retrieval Tasks. On high AOS tasks the Hyperbolic showed faster performance, but on low AOS tasks the Windows File Browser showed faster performance. Experiment 2 focused only on the Retrieval tasks and revealed that Hyperbolic Tree users examined more tree nodes at a faster rate and visually searched through the tree hierarchy at a faster rate than users of a Windows File Browser lookalike, however, visual search paths were shortened in dense areas of the Hyperbolic Tree display when information scent was low. Two processes appear to affect visual search in the Hyperbolic display: strong information scent improves visual search, and the crowding of targets in a compressed region degrades visual search especially when there is weak information scent.

We have developed a methodology for studying and analyzing the psychology of users performing ecologically valid WWW tasks. A user trace is a record of all significant states and events in the user-WWW interaction based on eye tracking data, application-level logs, and think-aloud protocols. A user-tracing architecture has been implemented for developing simulation models of user-WWW interaction and for comparing a simulation model (SNIF-ACT) against user-trace data. The user tracing architecture compares each action of the SNIF-ACT simulation directly against observed user actions. The model and architecture have been used to successfully match detailed user trace data from four users working on two tasks each.

Abstract We introduce a technique for creating novel, enhanced thumbnails of Web pages. These thumbnails combine the advantages of plain thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. As expected, there was a strong effect of question category. For some questions, text summaries outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text summaries. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) had more consistent performance than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

The purpose of this paper is to introduce a replicable WWW protocol analysis methodology illustrated by application to data collected in the laboratory. The methodology uses instrumentation to obtain detailed recordings of user actions with a browser, caches Web pages encoutered, and videotapes talk-aloud protocols. We apply the current form of the method to the analysis of eight Web protocols, visualizing the structure of the interaction and showing the strong effect of information scent in determining the path followed.

On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting pints, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.

Eye tracking studies of the Hyperbolic Tree browser suggest that visual search in focus+context displays is highly affected by information scent (i.e., the proximal cues to the value of distal information). When users detected a strong information scent, they were able to reach their goal faster with the Hyperbolic Tree browser than with a conventional browser. When users detected a weak scent or no scent, they were less efficient in visually searching the areas of the Hyperbolic display that had a higher density of visual items. Two countervailing processes appear to affect visual attention in the Hyperbolic display: (1) Strong information scent expands the spotlight of attention, whereas (2) the crowding of targets in a compressed region narrows it. We interpret these results through an integration of the CODE Theory of Visual Attention with information foraging theory. Keywords Focus+context, Hyperbolic Tree, information foraging, information scent, information visualization, visu...

We introduce a technique for creating novel, textually-enhanced thumbnails of Web pages. These thumbnails combine the advantages of image thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. We found a strong effect of question category. For some questions, text outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) were more consistent than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

Focus + context information visualizations have sought to amplify human cognition by increasing the amount of information immediately available to the user. We study how the focus + context distortion of the Hyperbolic Tree browser affects information foraging behavior in a task similar to the CHI '97 Browse Off. In comparison to a more conventional browser, Hyperbolic users searched more nodes, searched at a faster rate, and showed more learning. However, the performance of the Hyperbolic was found to be highly affected by “information scent”, proximal cues to the value of distal information. Strong information scent made hyperbolic search faster than with a conventional browser. Conversely, weak scent put the hyperbolic tree at a disadvantage. There appears to be two countervailing processes affecting visual attention in these displays: strong information scent expands the spotlight of attention whereas crowding of targets in the compressed region of the Hyperbolic narrows it. The results suggest design improvements.

Designers and researchers of users' interactions with the World Wide Web need tools that permit the rapid exploration of hypotheses about complex interactions of user goals, user behaviors, and Web site designs. We present an architecture and system for the analysis and prediction of user behavior and Web site usability. The system integrates research on human information foraging theory, a reference model of information visualization and Web data-mining techniques. The system also incorporates new methods of Web site visualization (Dome Tree, Usage Based Layouts), a new predictive modeling technique for Web site use (Web User Flow by Information Scent, WUFIS), and new Web usability metrics.

Information Foraging (IF) Theory addresses user strategies and technology for seeking, gathering, and using on-line information. We present IF-based models and evaluations of two interfaces: the Scatter/Gather browser for large document collections, and the Butterfly interface for surfing the citation link structure of scientific literatures. A computational cognitive model, ACT-IF, models observed users by assuming that they have heuristics that optimize their information foraging behavior in accordance with IF theory.

Several visualizations have emerged which attempt to visualize all or part of the World Wide Web.Those visualizations, however, fail to present the dynamically changing ecology of users and documents on the Web.We present new techniques for Web Ecology and Evolution Visualization (WEEV).Disk Trees represent a discrete time slice of the Web ecology.A collection of Disk Trees forms a Time Tube, representing the evolution of the Web over longer periods of time.These visualizations are intended to aid authors and webmasters with the production and organization of content, assist Web surfers making sense of information, and help researchers understand the Web.

Designers and researchers of human-computer interaction need tools that permit the rapid exploration aa management of hypotheses about complex interactions of designs, task conditions, and user strategies.Dynamic programming is introduced as a such a tool for the analysis of information foraging technologies.The technique is illustrated in the context of the Scatter/Gather text clustering browser.Hypothetical improvements in browser speed ma text clustering are examined in the context of variations in task deadlines and the quality of the document repository.A complex and non-intuitive set of tradeof% emerge from even this simple space of factors, illustrating the general utility ofthe approach.

An ecological-cognitive fimnework of analysis and a model-tracing amhitecture are presented and used in the analysis cf data recorded flom users browsing a large document collection. The usem interacted with the Scatter/Gather browser, which clusters documents into groups of similar content and presents users with summaries of cluster content. Predictions made by a computational model d navigation and information foraging are matched against the observed activity.

To facilitate users' ability to make sense of large collections of hypertext we present two new techniques for inducing clusters of related documents on the World Wide Web. Users' ability to find relevant information might also be enhanced by finding lawful properties of document behavior and use. We present models and analyses of document use and change for the World Wide Web. Keywords Clustering, categorization, co-citation analysis, World Wide Web, hypertext, survival analysis, usage models INTRODUCTION The ever-increasing universe of electronic information competes for the effectively fixed and limited attention of people. Both consumers and producers of information want to understand what kinds of information are out there, how desirable it is, and how its content and use change through time. Our work aims to discover empirical regularities of hypertext content, use, and structure, and ways of exploiting these regularities to provide new ways of helping people to find and make...

In its current implementation, the World-Wide Web lacks much of the explicit structure and strong typing found in many closed hypertext systems.While this property probably relates to the explosive acceptance of the Web, it further complicates the already difficult problem of identifying usable structures and aggregates in large hypertext collections.These reduced structures, or localities, form the basis for simplifying visualizations of and navigation through complex hypertext systems.Much of the previous research into identifying aggregates utilize graph theoretic algorithms based upon structural topology, i.e., the linkages between items.Other research has focused on content analysis to form document collections.This paper presents our exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.Linear equations and spreading activation models are employed to arrange Web pages based upon functional categories, node types, and relevancy.

Scatter/Gather is a cluster-based browsing technique for large text collections.Users are presented with automatically computed summaries of the contents of clusters of similar documents and provided with a method for navigating through these summaries at different levels of granularity.The aim of the technique is to communicate information about the topic structure of very large collections.We tested the effectiveness of Scatter/Gather as a simple pure document retrieval tool, and studied its effects on the incidental learning of topic structure.When compared to interactions involving simple keyword-based search, the results suggest that Scatter/Gather induces a more coherent conceptual image of a text collection, a richer vocabulary for constructing search queries, and communicates the distribution of relevant documents over clusters of documents in the collection.

We present a new focus+context (fisheye) technique for visualizing and manipulating large hierarchies. Our technique assigns more display space to a portion of the hierarchy while still embedding it in the context of the entire hierarchy. The essence of this scheme is to lay out the hierarchy in a uniform way on a hyperbolic plane and map this plane onto a circular display region. This supports a smooth blending between focus and context, as well as continuous redirection of the focus. We have developed effective procedures for manipulating the focus using pointer clicks as well as interactive dragging, and for smoothly animating transitions across such manipulation. A laboratory experiment comparing the hyperbolic browser with a conventional hierarchy browser was conducted.

Article Free Access Share on Information foraging in information access environments Authors: Peter Pirolli Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CAView Profile , Stuart Card Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CAView Profile Authors Info & Claims CHI '95: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsMay 1995 Pages 51–58https://doi.org/10.1145/223904.223911Published:01 May 1995Publication History 231citation4,388DownloadsMetricsTotal Citations231Total Downloads4,388Last 12 Months767Last 6 weeks210 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

In this paper we present a method, the Cost-of-Knowledge Characteristic Function, for characterizing information access from dynamic displays.The paper works out this method for a simple, but important, class of dynamic displays called direct-walk interactive information visuatizations, in which information is accessed through a sequence of mouse selections and key selections.The method is used to characterize a simple calendar task for an application of the Information Visualizer, to compute the changes in characterization as the result of possible program variants, and to conduct empirical comparison between different systems with the same function.

This article discusses cognitive models of learning to program recursion and their relation to lessons on recursion in an intelligent computer tutor for LISP programming (the LISP Tutor). The cognitive models are implemented as production systems in which programming skill is characterized as the decomposition of programming goals into subgoals and elementary actions via the application of programming plans. Two sets of learning mechanisms are used in the cognitive models. Analogical problem-solving mechanisms use declarative knowledge of example program solutions to overcome problem-solving impasses. Knowledge compilation mechanisms summarize problem solutions into efficient problem-solving skill. Analyses and simulations of novice and expert programming were used to develop ideal models of the programming knowledge to confer upon students and bugs that characterize common misconceptions. The LISP Tutor uses the ideal models and bugs to guide its interactions with students. Experimental evaluations of the LISP Tutor indicate that it is more efficient and effective than classroom instruction.