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Yellow Nutsedge (Cyperus esculentus L.) belongs to the family of Cyperaceae. It propagates exclusively with tubers in the ground. Its abundance has strongly increased in Switzerland in the last two decades. Main reasons for this increase are changing land use and production systems, a difficult herbicide control and a low awareness level among the farmers. The actual invasion is enhanced by unintended dislocation of tubers by machines or crops/crop waste, lack of knowledge of infested fields and missing supporting measures. An obligation to announce infested fields and to control Yellow Nutsedge is currently discussed in Switzerland. Such an obligation would help contractors and buyers of crops to reduce unintended dislocation of tubers. Cleaning up infested plots is quickly expensive and complex. Maleic acid hydrazide is actually used for preventing potato tubers to germinate in stock. We tested Fazor (maleic acid hydrazide) and Asulox (asulam) for its efficacy on preventing germination of Yellow Nutsedge tubers. The results of our pot trial in the greenhouse are clear. The expected efficiency was not observed. Furthermore the complexity of problems with this species in agriculture is discussed and some ideas for a control strategy are given in this paper.

Pedagogical agents have been designed to support the significant challenges that learners face when self-regulating in advanced learning environments. Evidence suggests differences in learners' prior skills and abilities, in conjunction with excessive didactic support, can cause overreliance on these external aids, which in turn prevents deeper learning, and pedagogical agents can provide tailored scaffolding to accommodate learners' individual needs. However, there is less evidence about the impact of abstract scaffolding, such as the sharing of non-verbal metacognitive information via a pedagogical agent's facial expressions, on self-regulated learning. To assess factors in the passing of non-verbal metacognitive information via pedagogical agents in a multimedia learning environment, we used growth modeling with self-reports, eye-tracking, and log-file data collected from fifty (n = 50) undergraduates at a large North American university as they learned about human body systems while using MetaTutor-IVH, a multimedia learning environment with a pedagogical agent. We controlled for participant characteristics (perceived utility of emotions for self- and other-centered positive and negative emotions) and characteristics of the metacognitive monitoring information provided by a pedagogical agent (expression type and expression congruency) to assess factors in non-verbally communicating metacognitive information. Results suggest that learners attend to pedagogical agents less over time, but this rate of change is weaker when an agent is providing an expression that is congruent with the ground truth of the environment. Further, only the perceived information utility of other-centered negative emotions has a significant effect on this duration, suggesting learners are driven to consult pedagogical agents to avoid embarrassment or shame. We discuss design implications of these findings for technology-based learning environments.

Reflection is critical for adolescents' problem solving and learning in game-based learning environments (GBLEs). Yet challenges exist in the literature because most studies lack a theoretical perspective and clear operational definition to inform how and when reflection should be scaffolded during game-based learning. In this paper, we address these issues by studying the quantity and quality of 120 adolescents' written reflections and their relation to their learning and problem solving with Crystal Island, a GBLE. Specifically, we: (1) define reflection and how it relates to skill and knowledge acquisition; (2) review studies examining reflection and its relation to problem solving and learning with emerging technologies; and (3) provide direction for building reflection prompts into GBLEs that are aligned with the learning goals built into the learning session (e.g., learn about microbiology versus successfully solve a problem) to maximize adolescents' reflection, learning, and performance. Overall, our findings emphasize how important it is to examine not only the quantity of reflection but also the depth of written reflection as it relates to specific learning goals. We discuss the implications of using game-learning analytics to guide instructional decision making in the classroom.

The goal of this chapter is to propose a cyclical process of how teachers can use multimodal multichannel data of cognitive, affective, metacognitive, motivational, and social processes to assist with the understanding of their own and their students' self-regulated learning (SRL), and their subsequent instructional decision making. What differentiates our proposed framework from theories of self-regulated learning is that we propose this cycle occurs throughout all phases of self-regulated learning and in real time. In this chapter, we outline each component of the framework, followed by a discussion of future steps that discuss our future view of the classroom with teachers and how they can use multimodal multichannel data to help them with their instructional decision making. We aim to make it clear that: (1) our proposed stages are non-linear and interdependent, which represents the foundation of SRL theory and the structure of classroom learning, and (2) our proposed framework highlights that using any technological tool to help teachers, not replace them.

Game-based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game-based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine-grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game-based learning environment for microbiology, Crystal Island, we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post-test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question-related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions.

Self-regulated learning (SRL), learners' monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by prompting and scaffolding learners to engage in SRL via using pedagogical agents. However, current literature does not examine the extent to which learners' deployed strategies are functional or dysfunctional in relation to pedagogical agent scaffolding. The current study collected 117 undergraduate students' data as they learned with MetaTutor, an ITS about the human circulatory system. Participants were randomly assigned to either the (1) Prompt and Feedback Condition where pedagogical agents scaffolded cognitive and metacognitive SRL strategies or (2) Control Condition where no prompts or feedback were provided. Results demonstrated that learners who received prompts by the pedagogical agents to engage in SRL had higher learning gains as well as greater frequencies across most strategies compared to those in the Control Condition who relied on self-initiated strategy use. While sequential transitions across all strategies were not significant between conditions, further analysis grounded in Complex Systems Theory found that learners who were prompted to engage in strategies demonstrated a significantly lower degree of repetition and balance between repetitive and novel patterns of strategy use. The findings suggest that pedagogical agents within MetaTutor successfully scaffolded the functional deployment of cognitive and metacognitive SRL strategies and are indicative of higher learning after interacting with ITSs.

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 study examined 57 learners' emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners' prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text comprehension. Analyses found that both discrete and non-discrete emotions were expressed during reading and answering in-game assessments of single-text comprehension. Learners expressed greater joy during reading and greater expressions of anger, confusion, and frustration during in-game assessments. Further results found that learners who expressed a high number of different emotions throughout reading and completing in-game assessments tended to have lower in-game comprehension scores whereas a higher number of different expressed emotions while completing in-game assessments was associated with greater posttest comprehension. Finally, while increased prior knowledge was associated with higher single- and multiple-text comprehension, there was no interaction between prior knowledge and emotions on multiple-text comprehension. Overall, this study found that (1) learners often express more than one emotion during GBLE activities, (2) emotions expressed while learning with a GBLE shift across different activities, and (3) emotions are related to demonstrated comprehension, but the type of activity influences this relationship. Results from this study provide implications for how emotions can be examined as learners engage in GBLE activities as well as the design of GBLEs to support learners' emotions accounting for different activity demands to increase comprehension of single and multiple texts.

The aim of this study was to predict university students' learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.

Emotional experiences have a significant impact on learningabout complex topics. Yet, challenges exist becauseemotions are typically operationalized as end products,excluding if, how, and when emotions change duringlearning and their relation to metacognition and performancewith advanced learning technologies such as intelligenttutoring systems (ITSs). In this paper, we addressed thesechallenges by capturing and analyzing 117 college students’concurrent and self-reported emotions at 3 time points duringlearning with MetaTutor, an ITS. Analyses revealed negativerelationships between increases in boredom, metacognitivemonitoring accuracy, and performance. We also foundthat if confusion persisted over time during learning, itwas detrimental to performance. These findings provideimplications for designing affect-sensitive ITSs which fosteremotion-regulation and metacognitive monitoring based onchanges in emotions during learning to optimize performance.

Theoretical models of self-regulated learning highlight theimportance and dynamic nature of metacognitive monitoringand regulation. However, traditional research typically has notexamined how different judgments, or the relative timing ofthose judgments, influence each other, especially in complexlearning environments. We compared six statistical modelsof performance of undergraduates (n = 55) learning inMetaTutor-IVH, a multimedia learning environment. Threetypes of prompted metacognitive judgments (ease of learning[EOL] judgments, content evaluations [CEs], and retrospectiveconfidence judgments [RCJs]) were used as individualpredictors, and combined in a uniformly-weighted compositescore and empirically based weighted composite score acrossthe learning session. The uniformly weighted composite scorebetter captured performance than the models using only anEOL judgment or RCJ judgment. However, the empiricallyweighted composite model outperformed all other models.Our results suggest that metacognitive judgments should notbe considered as independent phenomenon but as an intricateand interconnected process.

Learners ability to effectively monitor and apply cognitive (e.g., reading) and metacognitive (e.g., content evaluations)strategies in game-based learning environments (GBLEs) are influenced by internal factors such as prior knowledge. Thisstudy examined whether there were differences in learners strategy usage over time during learning with Crystal Island,a GBLE for microbiology, between high and low prior knowledge groups. Results indicated that learners with high priorknowledge had greater posttest scores, but spent less time reading. This is further influenced by relative time in gamewhere learners with high prior knowledge have greater reading durations at the start of the game, but smaller durationstowards the end compared to low prior knowledge learners. Learners’ metacognitive strategy usage did not differ betweenprior knowledge groups, but the use of this strategy increased over time. Implications for designing adaptive GBLEs fromlearners cognitive and metacognitive strategy use are discussed.

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.

Game-based learning environments (GBLEs) focus on enhancing learning by providing learners with various representations of information (e.g., text, diagrams, etc.) while allowing full autonomy, or control over their actions. Challenges arise as research shows that learners inaccurately use cognitive and metacognitive processes when given full autonomy. This study examined 105 undergraduates who were randomly assigned to autonomy conditions (i.e., full, partial, and no autonomy) as they interacted with scientific informational text presentations (i.e., non-player characters [NPCs], books and research articles, posters) during learning with Crystal Island, a GBLE. We assessed how learners' eye-tracking (e.g., fixation durations on objects) and log-file (e.g., durations of activities) data reflected how learners interacted with text presentations and selected pretest-relevant items (i.e., text providing answers to questions on the pretest). Results showed that participants in the partial autonomy condition (n = 38) demonstrated higher learning gains than those in the full autonomy condition (n = 45). Time spent interacting with all books and research articles within Crystal Island were positively correlated with learning gains. There were significant differences in learners' duration and fixation duration on informational text presentation interactions between conditions and within types of presentations as well as significant interactions between pretest-relevant items and types of presentations. Overall, autonomy and pretest relevancy impact the time interacting with informational text presentations which influence learning. Implications are provided for applying autonomy during game-based learning, and how this may direct future implementations of AI within GBLEs to provide implicit scaffolding via adaptively limiting learners' autonomy as they interact with informational text.