From product to process data: Game mechanics for science learning

Game design is a complex process based on balance between educational and gameplay experience in a coherent way. In the context of instructional design, Constructive Alignment aims to develop an outcome-based approach for designing learning activities during which the learners’ engagement through the activity is aligned with learning objectives and outcomes. Constructive Alignment focuses on the proposal of constructively aligned learning experiences, where assessment is the result of specially selected training activities, which are proposed based on intended learning outcomes. In this study, we analyze the game design process in an online master course on Game Design from a Constructive Alignment approach. We operationalize the Constructive Alignment in game design as the coherence between Learning Objectives, Game Mechanics, Learning Mechanics and the Assessment in the educational DGBL. The results analysis shows that the Game Based Learning course had a positive impact on students regarding the proposal of game design documents with coherence between Game Mechanics and Learning Mechanics as well as Learning Mechanics and Learning Objectives, while little focus on integrating Assessment mechanisms was observed. Based on the results of this study we propose to increase the focus on assessment in the process of game design.

Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students’ in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to anticipate student outcomes and proactively deliver adaptive scaffolding or notify instructors. Reflection is a key component of self-regulated learning, and it is critical in effective learning. However, there is currently limited work exploring the utility of reflection for inducing accurate predictive student models. This article presents a predictive student modeling framework that leverages natural language responses to in-game reflection prompts to predict student learning outcomes in a game-based learning environment for middle school microbiology, CRYSTAL ISLAND. With data from a pair of classroom studies involving 118 middle school students, we investigate the accuracy of early prediction models that utilize features extracted from student trace data combined with word embedding-based representations (i.e., GloVe, ELMo) of student reflection responses. We evaluate the accuracy of the predictive models over time using data from incremental segments of each student’s interaction with the game-based learning environment, and we compare against models that omit student reflection features. Results reveal that models encoding students’ natural language reflections with ELMo word embeddings yield significantly improved accuracy compared to other representations, with the greatest accuracy demonstrated by an ensemble of predictive models. We discuss the implications of these results for the design of game-based learning environments.

This paper offers an approach to designing game-based learning experiences inspired by the Mechanics-Dynamics-Aesthetics (MDA) model (Hunicke et al., 2004) and the elemental tetrad model (Schell, 2008) for game design. A case for game based learning as an active and social learning experience is presented including arguments from both teachers and game designers concerning the value of games as learning tools. The MDA model is introduced with a classic game- based example and a non-game based observation of human behaviour demonstrating a negative effect of extrinsic motivators (Pink, 2011) and the need to closely align or embed learning outcomes into game mechanics in order to deliver an effective learning experience. The MDA model will then be applied to create a game based learning experience with the goal of teaching some of the aspects of using source code control to groups of Computer Science students. First, clear aims in terms of learning outcomes for the game are set out. Following the learning outcomes, the iterative design process is explained with careful consideration and reflection on the impact of specific design decisions on the potential learning experience. The reasons those decisions have been made and where there may be conflict between mechanics contributing to learning and mechanics for reasons of gameplay are also discussed. The paper will conclude with an evaluation of results from a trial of computer science students and staff, and the perceived effectiveness of the game at delivering specific learning outcomes, and the approach for game design will be assessed.

The use of computer games in education has been increasing in popularity during the past decade. Game-based learning environments are designed to teach specific knowledge content and skill-based learning outcomes using game elements. One main reason for using game-based learning environments is to increase student motivation and engagement while teaching learning outcomes. Many of the game-based learning environments are designed so that students will reach maximum flow, which is defined as students being so completely immersed in that game that they do not notice that they are learning. These learning environments have been shown to improve many behaviour and cognitive learning outcomes. While game-based learning has many benefits, some educational researchers have indicated that it is often very costly to develop a complex game-based assessment to teach a few learning outcomes. Hence, in some cases it is more beneficial to approach the use of computer games in education using gamification.

BackgroundPrompted by fewer females compared to males enrolling in physics and advanced mathematics at both secondary and university levels, our research investigated the views and experiences of female students currently studying upper secondary school physics. We interviewed 18 female students about influences they considered important to their own science education, interest in science, and future science-related aspirations. Our purpose was to identify the experiences that these students most strongly associated with the generation and maintenance of their engagement in science, particularly represented in this research by their enrolment in upper secondary physics.ResultsThe research team used a systematic, iterative process to identify the main themes in the transcribed interview data. We identified the influence each girl reported as the strongest (ranked first). We also combined all influences that the participants had nominated, regardless of their ranking, to further examine all factors participants suggested as influential in their sustained engagement in school science (represented by their decision to study upper secondary physics). Systematic analysis of the interview data confirms that the influences on these females’ choices to study physics at upper secondary originate from a combination of their teachers, their school’s science culture, members of their family, the participants themselves and their peers.ConclusionsThe interviews highlighted the idiographic complexities in understanding the wide range of important influences on these students studying physics at upper secondary school and their engagement in science. The unique contribution of this work is giving voice to the participants and reflecting on what these high-achieving females have to say about the influential factors in their decisions to pursue science. Supportive teachers and the school science culture play essential roles, and other cultural and/or social factors such as family members and peers are identified as important. References to the culture and expectations of the school, family holidays, and conversations with siblings are support factors that seem to interact and overlap. At the same time, the importance of policy-amenable factors such as competent and caring science teachers, and science-supportive school cultures should be emphasised and encouraged.

Problem. A significant segment of the U.S. population, under-represented students, is under-engaged or disengaged in secondary science education. International and national assessments and various research studies illuminate the problem and/or the disparity between students’ aspirations in science and the means they have to achieve them. To improve engagement and address inequities among these students, more contemporary and/or inclusive pedagogy is recommended. More specifically, multicultural science education has been suggested as a potential strategy for increased equity so that all learners have access to and are readily engaged in quality science education. While multicultural science education emphasizes the integration of students’ backgrounds and experiences with science learning , multimedia has been suggested as a way to integrate the fundamentals of multicultural education into learning for increased engagement. In addition, individual characteristics such as race, sex, academic track and grades were considered. Therefore, this study examined the impact of multicultural science education, multimedia, and individual characteristics on under-represented students’ engagement in secondary science. Method. The Under-represented Students Engagement in Science Survey (USESS), an adaptation of the High School Survey of Student Engagement, was used with 76 highschool participants. The USESS was used to collect pretest and posttest data concerning their types and levels of student engagement. Levels of engagement were measured with Strongly Agree ranked as 5, down to Strongly Disagree ranked at 1. Participants provided this feedback prior to and after having interacted with either the multicultural or the nonmulticultural version of the multimedia science curriculum. Descriptive statistics for the study’s participants and the survey items, as well as Cronbach’s alpha coefficient for internal consistency reliability with respect to the survey subscales, were conducted. The reliability results prompted exploratory factory analyses, which resulted in two of the three subscale factors, cognitive and behavioral, being retained. One-within one-between subjects ANOVAs, independent samples t-test, and multiple linear regressions were also used to examine the impact of a multicultural science education, multimedia, and individual characteristics on students’ engagement in science learning. Results. There were main effects found within subjects on posttest scores for the cognitive and behavioral subscales of student engagement. Both groups, using their respective versions of the multimedia science curriculum, reported increased engagement in science learning. There was also a statistical difference found for the experimental group at posttest on the measure of “online science was more interesting than school science.” All five items unique to the posttest related to the multimedia

Game-based learning has gained significant attention from educational researchers because of its ability to create an engaging and enjoyable learning environment for students. However, there was a research gap regarding the design of game mechanics that specifically helped students understand abstract scientific concepts. Also, the impact of increased motivation, interest, and engagement on conceptual understanding remained uncertain. The objective of this study was, therefore, to analyze the efficacy of using analogy teaching as a learning design principle for creating game mechanics to improve students' conceptions of bioaccumulation and biomagnification. Additionally, the study aimed to investigate whether providing a clear analogy comparison after gameplay could have a significant impact on students' learning outcomes. The research also examined the correlation between students' enjoyment level during gameplay and their learning outcomes. An embedded experimental mixed methods design was utilized to address the research objectives. The participants were 54 undergraduate students from non-science disciplines. They were divided into two groups: the Game-based Learning [GL] group and the Game-based Learning with eXplicit analogy discussion [GLX] group. Both groups used a board game to learn about bioaccumulation and biomagnification. The GL group had reflective discussions about the concepts after playing the game. The GLX group had an additional task where they identified similarities and differences between the gameplay and the scientific concepts, and they explicitly discussed these through a worksheet provided before the discussion. Results showed that after engaging in game-based learning, both groups exhibited a significant improvement in their understanding of bioaccumulation and biomagnification, moving from incomplete or partial understanding to more scientifically accurate concepts. Notably, the GLX group scored higher in post-tests compared to the GL group. Despite high levels of enjoyment during gameplay, there was no correlation between this enjoyment and the post-test scores. An unexpected slight negative correlation was found between immersion during the game and post-test scores. Discussion and implementation of the research results were provided.

Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning.

This paper presents the design, development and analysis of a game for computer literacy. Specifically, the game addresses educational objectives included in the curriculum of non-technological careers, such as the identification and analysis of concepts related to basic hardware components of a computer. These objectives are joined with those of a video game, i.e., live an attractive, motivating and enriching experience. The game has focused on the interaction between the player, who plays the role of a student, a 3D virtual computer, and a character who plays the role of the teacher. The player interacts with the computer and answers a series of inquiries posed by the teacher, all related to components of the 3D computer located in the game scenario, which represents the teacher's office. For each correct answer, the player receives a sounded feedback and messages of encouragement from the teacher, as well as points. Once the player completes each level, the result is stored and can be displayed in a scoreboard. Game analysis has been done using the so-named LM-GM framework, which is an analytical model proposed by GaLA (Game and Learning Alliance, the European Network of Excellence on Serious Games), that maps learning mechanics (LM) to game mechanics (GM). The results of the analysis indicate that the serious game seemly integrates learning objectives as well as LM and GM.

Modeling student knowledge is critical in adaptive learning environments. Predictive student modeling enables formative assessment of student knowledge and skills, and it drives personalized support to create learning experiences that are both effective and engaging. Traditional approaches to predictive student modeling utilize features extracted from students’ interaction trace data to predict student test performance, aggregating student test performance as a single output label. We reformulate predictive student modeling as a multi-task learning problem, modeling questions from student test data as distinct “tasks.” We demonstrate the effectiveness of this approach by utilizing student data from a series of laboratory-based and classroom-based studies conducted with a game-based learning environment for microbiology education, Crystal Island. Using sequential representations of student gameplay, results show that multi-task stacked LSTMs with residual connections significantly outperform baseline models that do not use the multi-task formulation. Additionally, the accuracy of predictive student models is improved as the number of tasks increases. These findings have significant implications for the design and development of predictive student models in adaptive learning environments.

Student engagement stands as a critical determinant of academic success and learning outcomes, particularly in the realm of science education, where active participation and enthusiasm significantly impact knowledge acquisition and retention. This study aimed to enhance the engagement of Grade 7 students in science classes using the Science Workstation Strategy in one of the public schools in Ozamiz City during the academic year 2023-2024. The study utilized a classroom-based action research design. Thirty - five participants were selected through purposive sampling during the A.Y. 2023-2024 in a public school in Ozamiz City. The study used a researcher-made test and Minitab statistical software, utilizing tools such as mean, standard deviation, and t-test. The study yielded the following findings: (1) On the average, students’ engagement displayed a high level of engagement before using the workstation strategy; (2) The students’ engagement after using the Science Workstation Strategy improved significantly. They performed within the Moderately High range; (3) There was a significant difference in students’ engagement before and after using the Science Workstation Strategy; and (4) There are other observed improvements among the students after the use of the Science Workstation Strategy. In conclusion, using the science workstation strategy effectively improved students’ engagement in science and enhance learning experiences. This supports a recommendation in adopting this strategy as teaching method to improve student engagement in science classes.

Students’ performance and motivation are influenced by their emotions. Game-based learning (GBL) environments comprise elements that facilitate learning and the creation of an emotional connection with students. GBL environments include Intelligent Tutoring Systems (ITSs) to ensure personalized learning. ITSs reason about students’ needs and characteristics (student modeling) to provide suitable instruction (tutor modeling). The authors’ research is focused on the design and implementation of an emotional student model for GBL environments based on the Control-Value Theory of achievement emotions by Pekrun et al. (2007). The model reasons about answers to questions in game dialogues and contextual variables related to student behavior acquired through students’ interaction with PlayPhysics. The authors’ model is implemented using Dynamic Bayesian Networks (DBNs), which are derived using Probabilistic Relational Models (PRMs), machine learning techniques, and statistical methods. This work compares an earlier approach that uses Multinomial Logistic Regression (MLR) and cross-tabulation for learning the structure and conditional probability tables with an approach that employs Necessary Path Condition and Expectation Maximization algorithms. Results showed that the latter approach is more effective at classifying the control of outcome-prospective emotions. Future work will focus on applying this approach to classification of activity and outcome-retrospective emotions.

Inquiry-based learning has been proposed as a natural and authentic way for students to engage with science. Inquiry-based learning environments typically require students to guide their own learning and inquiry processes as they gather data, make and test hypotheses and draw conclusions. Some students are highly self-regulated learners and are able to guide and monitor their own learning activities effectively. Unfortunately, many students lack these skills and are consequently less successful in open-ended, inquiry-based environments. This work examines differences in inquiry behavior patterns in an open-ended, game-based learning environment, Crystal Island. Differential sequence mining is used to identify meaningful behavior patterns utilized by Low, Medium, and High self-regulated learners. Results indicate that self-regulated learners engage in more effective problem solving behaviors and demonstrate different patterns of use of the provided cognitive tools. The identified patterns help provide further insight into the role of SRL in inquiry-based learning and inform future approaches for scaffolding.

Although there is a consensus on the instructional potential of Serious Games (SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance (http://www.galanoe.eu) European Network of Excellence on Serious Games, which has a focus upon pedagogy‐driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of gameplay, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics–Game Mechanics (LM‐GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in an SG. The LM‐GM model includes a set of pre‐defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories.Designers and analysts can exploit these mechanics to draw the LM‐GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework's support in determining how gameplay and pedagogy intertwine in an SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state‐of‐the‐art framework.

In this study we focus on the integration of mathematical learning content (i.e., proportional reasoning) in game-based learning environments (GBLE). More specifically, two kinds of GBLEs are set up: an extrinsically integrated GBLE and an intrinsically integrated GBLE. In the former environment, the mathematical content is not part of the core mechanics and structure of the gaming world. In the latter environment, the mathematical content is delivered through the parts of the game that are the most fun to play and embodied within the structure of the gaming world and the players’ interactions with it. Sixty-four vocational track students participated in the study, all of them working in either version of the self-developed GBLE “Zeldenrust”. The results of this study suggest that the way the content is integrated in a GBLE (i.e., intrinsically or extrinsically) matters: contrary to our expectations, students who played the extrinsically integrated game showed higher learning gains, motivational gains and perceived usefulness than students who played the game in which the content was intrinsically integrated.