Intelligent and interconnected devices, so-called smart devices, have been appearing in almost all areas of life for several years. Under the catchword Internet of Things, these devices, the underlying infrastructures and processes offer more convenience in everyday areas. In the meantime, they are increasingly finding their way into the home living room, where they are referred to as Smart Home. According to Satpathy, the Smart Home is “a home which is smart enough to assist the inhabitants to live independently and comfortably with the help of technology […]. In a smart home, all the mechanical and digital devices are interconnected to form a network, which can communicate with each other and with the user to create an interactive space” [1]. The devices are therefore increasingly interconnected and hardly present for the users, so that an all-pervasive networking of everyday life occurs through the use of intelligent objects in the sense of a pervasive environment [2].

The so-called Intelligent Personal Assistants (IPAs) represent a Natural User Interface (NUI) to the Smart Home. IPAs have also been integrated into operating systems and devices, such as Apple Siri , Samsung Bixby or Google Assistant . IPAs offer a variety of functionalities that are constantly growing. These so-called skills can be used to execute a variety of functions to retrieve information or to control smart home devices. For this purpose, the voice input is transferred to a corresponding cloud infrastructure of the respective provider. The voice recording is semantically analyzed and then corresponding functionalities are executed in the cloud, on the local hardware or on connected smart devices. Through the collection of user data and a subsequent analysis using artificial intelligence, an attempt is made to simplify the daily life of the users by offering contextual support, functions and recommendations. In the meantime, specialized hardware is also offered, such as Amazon Echo with its associated IPA Alexa , or Google Home with the IPA Google Assistant.

However, little attention has so far been paid to the support of informal and formal teaching and learning processes in the context of smart homes. The primary focus is still on energy management, entertainment, communication and home automation [3]. IPAs offer a possibility to rethink traditional learning with flashcards or with a classical learning partner in a familiar environment, with technology and interaction taking a back seat. But are IPAs even suitable to support independent learning and assessment at home?

This paper presents an approach to use IPAs based on the Amazon Echo and the associated IPA Alexa to design formal and informal learning in the form of a virtual tutor. For this purpose, the technical and didactic requirements as well as the special features of agent-based and pervasive learning with the help of NUIs are discussed. With the help of the Technology Acceptance Model (TAM), the implemented proof of concept is evaluated with regard to user acceptance. In doing so, the fundamental question of whether users would be prepared in principle to use such a technology for home learning will be investigated.

A didactic conception of the virtual tutor is crucially for the later acceptance by the users. For this purpose the didactic and technical components must be separated from each other and designed in a modular way so that teachers and learners can adapt the IPA to the teaching and learning scenario to their wishes as far as possible. As part of the requirements analysis, recent developments in the area of Intelligent Tutoring Systems (ITS) and the design recommendations applicable to ITS [4] were adapted for the design and implementation of the prototype. Furthermore current findings on teaching and learning research [5, 6] and virtual agents [4, 7] were used.

The implemented prototype consists of six loosely coupled components (see Fig. 3). The authoring system (1) is used to submit learning packages, learning objects and didactic design. These are then persisted in a database (2) specialized in XML files via the central RESTful web service (6). The IPA Client (3) can use the web service to update user profiles and to retrieve stored learning data from the database. A Learning Record Store (LRS) (4) is used to store the learning progress using xAPI statements. The overall architecture is completed by a recommendation component (5) for proposing suitable learning objects.

Only recognized standards were used to ensure reusability and adaptability of the system. The standards Sharable Content Object Reference Model (SCORM) and Common Cartridge (CC) by the IMS Global Learning Consortium (IMS) were shortlisted. Unlike SCORM, CC integrates the IMS Question and Test Interoperability (QTI) specification. With IMS Learning Design (LD), CC also offers the option of mapping the didactic setting and with the IMS Learner Information Package (LIP) the “learning outcomes” of learners. These functions are essential for the implementation of independent learning and assessment functions. The learning packages are mapped with IMS Content Packaging (CP) and the individual learning objects with QTI. IMS MD fully integrates the IEEE Learning Object Metadata (LOM) specification and adds further information such as language, keywords, subject area, format and difficulty to the learning objects specified in QTI. Therefore the IMS MD specification is used to manage the metadata. In the following sections, the single components of the system are now described in more detail.

The implemented solution is a novel scenario using a relatively new technology making it difficult to compare or classify effectiveness. Thus, the acceptance of the users regarding the use of the IPA for independent learning was first evaluated. It should be determined whether the users accept the conceived teaching and learning scenario with a virtual tutor at all and whether the prototype can be used for further developments.

In acceptance research the Technology Acceptance Model (TAM) has been proven to be a fundamentally reliable model for recording user acceptance with its various extensions (TAM 2/TAM 3) [10]. The TAM 2 extends the original TAM by adding determinants from the categories social inputs and cognitive instrumental processes. The social inputs include the input variables Subjective Norm (SN), i.e. the perception of the social environment, the social status (Image - IMG), the perceived influence of the social status through the use of technology, the experience (EXP) and the voluntariness (VOL). The category of cognitive instrumental processes includes job relevance (REL), i.e. the perception of how much the technology supports the task, the output quality (OUT) and the result demonstrability (DEMO).

The actual use or acceptance depends significantly on the two variables perceived usefulness (PU) and perceived ease of use (PEOU). Usefulness is the degree to which a person thinks that the use of the technology improves labour productivity. Usability describes the perceived effort required to learn. The variables of social status (IMG) and subjective norm (SN) were not considered for the study design. For the survey nine hypotheses, one for each variable, were stated. An adapted questionnaire of Venkatesh and Davis [10] was used and adapted to the study area of the virtual tutor (see Tab. 3).

The acceptance tests were conducted on 12 tech-savvy subjects (seven male, five female). At the beginning, the subjects were introduced to the basic handling of Amazon Echo and Alexa. A test series (cp. Demo.mp3) consisted of 18 learning objects and lasted about 25 minutes. The used learning design and the corresponding learning objects are listed in the content package [11] (cp. folder content ). The missing elements like the positive and negative feedback can be observed in the full content package.

The questions were then completed in the form of an online survey with a Likert scale of 1 (strongly in favor) to 5 (disagree).

The perceived usefulness (PU) was evaluated with a mean of 2.67 (σ = 0.58). Thus, the prototype could not convince clearly but is perceived as rather useful. The perceived ease of use (x̅ = 2, 27; σ = 0.58) suggests that the system is considered to be relatively user-friendly. The intention to use (ITU; x̅ = 2; σ = 0.71) indicates an intention to use with given availability. The relevance (REL) with a mean value of 3.33 (σ = 0.66) is rather negative, so that the virtual tutor does not seem to have any decisive relevance for learning (see Tab. 3 for all results).

For a better explanation of these input variables a correlation analysis according to Bravais-Pearson was then performed. The correlation results relevant to the hypothesis test are shown in Fig. 6. An additional regression analysis was carried out to identify the variables that make the highest contribution to the Intention to Use (ITU). All variables are suitable as predicator as expressed by the adjusted coefficient of determination (〖r ̅〗^2) (see Fig. 6).

The intended use (ITU) depends significantly on the previous experience (EXP; β = 0.90), the perceived added value (PU1; β = 0.88) and the perceived ease of use (PEOU3; β = 0.87).

However, a strong limitation of this study is that only 12 subjects were included. Therefore, no valid statistical conclusion can be drawn. The results are thus only to be understood as reference values and must be supplemented by further test subjects.

An IPA based on Amazon Alexa was designed and as a proof of concept implemented and evaluated. The technical and didactic principles, the requirements and the main components of the resulting architecture were briefly outlined. For developing the prototype, the latest technologies and recognized standards were used. The prototype represents an innovative approach in using the existing pervasive possibilities to design native teaching and learning scenarios in the form of a virtual tutor. The modular architecture ensures that a substitution of Amazon Alexa by an open-source component like Mycroft Mark II or Jasper is possible to address data privacy concerns.

The evaluation results using the technology acceptance model (TAM) should be viewed with caution due to the small number of test subjects. If the system is available, however, there seems to be an increased intention to use it. Amazon Echo with a predefined interaction model has limited possibilities to implement a free dialogue design. Thus, only short conversation scenarios and the realization of smaller learning units seem meaningful. Other limitations refer to the predefined interaction model and slot types, which limits the virtual tutor by compile time. Since the natural language unit uses pattern matching by probability calculation, wrong answers can be detected as correct (e.g. questions using the task type sequence with five answer options where the first three are reproduced by the learner in the correct order and the fourth and fifth option are in the wrong order).

The proposed prototype provides a narrowly defined range of functions. Enhancements are conceivable in which the IPA controls additional devices in the Smart Home in order to create a learning-friendly climate in the context of the physical and virtual environment. Different environmental settings for learning or for testing are possible. Further developments could deal with the adaptation and the integration of the environment. It is conceivable, for example, that the brightness level could be automatically adjusted by controlling darkening, that disturbing or distracting devices are switched off or put into mute mode, or that smart devices (tablet, smart TV) relevant for the respective learning scenario could be started. By including and analyzing the learning progress, it would also be conceivable to suggest breaks from learning or to offer further learning aids. In order to expand the didactic and recommendation functions, the Deep Learning infrastructure can be used for future work. When dealing with commercial cloud infrastructures, data protection aspects must still be taken into account.

1. Satpathy, L.: Smart Housing: Technology to Aid Aging in Place. New Opportunities and Challenges. Master’s Thesis, Mississippi State University, Starkville, MS, USA, 2006.

2. Abicht, L.; Spöttl, G.: Qualifikationsentwicklungen durch das Internet der Dinge: Trends in Logistik, Industrie und Smart House". Bertelsmann, 2012.

3. Wolff, A.: Smart Home Monitor 2017, 27. März 2018. https://www.splendid-research.com/smarthome.html (last visit 2019-08-22).

4. Sottilare, R.; Graesser, A.; Hu, X.; Goodwin, G.: Design Recommendations for Intelligent Tutoring System - Volume 5: Assessment Methods, 2017. https://gifttutoring.org/attachments/download/2410/Design%20Recommendations%20for%20ITS_Volume%205%20-%20Assessment_final_errata%20corrected.pdf (last check 2019-08-22)

5. Mayer, H. O.; Hertnagel, J.; Weber, H.: Lernzielüberprüfung im eLearning. deGruyter Oldenbourg, München, 2009.

6. Strzebkowski, R.: Selbständiges Lernen mit Multimedia in der Berufsausbildung - Medien-didaktische Gestaltungsaspekte interaktiver Lernsysteme. Diss., Berlin, 2006.

7. Cohen, M. H.; Giangola, J. P.; Balogh, J.: Voice User Interface Design. Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, USA, 2004.

8. Anderson, L. W.; Krathwohl, D. R. (Eds.): A Taxonomy for Learning, Teaching, and Assessing. A Revision of Bloom’s Taxonomy of Educational Objectives. Longman, New York, 2001.

9. Amazon: Build Skills with the Alexa Skills Kit, 27. März 2018, url: https://developer.amazon.com/docs/ask-overviews/build-skills-with-the-alexa-skills-kit.html (last check 2019-08-22).

10. Venkatesh, V.; Davis, F. D.: A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. In: Manage. Sci., Vol. 46/2, Feb. 2000, pp. 186-204, url: http://dx.doi.org/10.1287/mnsc.46.2.186.11926 . (last check 2019-08-22)

11. Kiy, A.; Wegner, D.: University-of-Potsdam-MM/Alexa: Alexa Skill (Version v1.0). Zenodo, 2018. http://doi.org/10.5281/zenodo.1441158 . (last check 2019-08-27)