Rainer Winkler

Smart Personal Assistants (SPAs; such as Amazon’s Alexa or Google’s Assistant) let users interact with computers in a more natural and sophisticated way that was not possible before. Although there exists an increasing amount of research of SPA technology in education, empirical evidence of its ability to offer dynamic scaffolding to enhance students problem-solving skills is still scarce. To fill this gap, the aim of this paper is to find out whether interactions with scaffolding-based SPA technology enable students to internalize and apply problem-solving steps on their own in a 10th grade high school and a vocational business school class. Students in the experiment classes completed their assignments using Smart Personal Assistants, whereas students in the control classes completed the same assignments using traditional methods. This study used a mixed-method approach consisting of two field quasi-experiments and one post-experiment focus group discussion. The empirical results revealed that students in the experiment classes acquired significantly more problem-solving skills than those in the control classes (Study 1: p = 0.0396, study 2: p < 0.001), and also uncovered several changes in students’ learning processes. The findings provide first empirical evidence for the value of using SPA technology on skill development in general, and on problem-solving skill development in particular.

Many governments and organizations recognize the potential of open innovation (OI) models to create value with large numbers of people beyond the organization. It can be challenging , however, to design an effective collaborative process for a massive group. Collaboration engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration engineers. To manage the complexity of the design process, they use a modeling technique called facilitation process models (FPM) to capture high-level design decisions that serve different purposes, such as documenting and communicating a design, etc. FPM, however, was developed to support designs for groups of fewer than 100 people. It does not yet represent design elements that become important when designing for groups of hundreds or thousands of participants, which can be found in many OI settings. We use a design science approach to identify the limitations of the original FPM and derive requirements for extending FPM. This article contributes to the CE and to the OI literature by offering an FPM 2.0 that assists CE designers to design new OI processes, with a special focus on outside-in OI.

Online education creates new opportunities for learners, which has led to sharply increasing enrollment in the last few years. Despite these benefits, past research shows that the lack of individual interaction with educators creates low learner engagement that leads to high attrition rates, which remains a major challenge in the field. Dynamically scaffolding conversational agents built into online video lectures promise to address this problem by individually interacting with learners, similar to educators’ scaffolding behavior. These agents are equipped with recent natural language processing capabilities, creating human-like conversations that help learners to be more engaged in the learning process. To test our hypothesis, we built a dynamically scaffolding conversational agent named Sara and compared it with an often-implemented static conversational agent built into two online video lectures. We deployed a lab experiment with 182 learners. The neurophysiological measurements revealed that Sara significantly improved learner engagement partly explained by differences in learners’ perceptions in the way they experienced the interaction. This study connects to already existing conversational agent studies in online education and highlights the importance of including dynamically scaffolding conversational agents in online video lectures to address the problem of low learner engagement in online education.

Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffolding learners’ understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.