IV.4 Design and Implementation of the Adaptivity Framework in Betty’s Brain
IV.4.2 Delivering Adaptive Scaffolds for different Trigger Conditions
Adaptive scaffolds from our framework are delivered by the virtual mentor agent Mr Davis present in the Betty’s Brain environment. One aspect of the agent behaviors defined in the learner scaffolding module in our framework is to query the priority queue for the highest priority trigger (strategy pattern or affect transition).
When the agent gets the highest priority element from the queue, this element forms the triggering condition for scaffolding. The agent then looks into a<triggering condition, conversation tree>map to extract the conversation tree corresponding to the triggering condition.
IV.4.2.1 Conversation Tree Representation for Scaffold Delivery
Conversation trees have been used to deliver contextualized and conversational learner scaffolds in past ex- perimental research using Betty’s Brain (Segedy et al.,2013;Munshi et al.,2022b). They facilitate back- and-forth conversation between the learner and the student that seeks to engage learners in authentic social interactions (Vygotsky,1978). The set of 9 conversation trees (3 Read→Build, 3 Quiz→Build, 1 Quiz→Read and 2 affective/strategic) developed in the final design of the adaptive scaffolding framework for this disser- tation are presented in FigureIV.4. Conversation trees developed for previous iterations of the scaffolding framework are present in AppendixB: this includes FigureB.1, used for Munshi et al.(2022b)), and Fig- ureB.2, used for Munshi et al.(2022a)). In all of these figures, we note that the conversation tree nodes contain the skeleton of the feedback to be offered to the learner for a particular trigger condition and at a specific scaffolding level (discussed later in this section). However, the content of the tree nodes contain places where additional task information, as determined from the learner’s current task and recent activities, is filled in to offer more relevant and contextualized feedback. This is illustrated in FigureB.1where we can compare actual contextualized scaffolds received by a student to the conversation tree structure that facilitated the delivery of the scaffold.
Conversations following the tree structure are initiated by one of the virtual agents. At the end of each piece of feedback within a tree node, the agent asks the learner if they require further guidance. The student can indicate their answer to this question by selecting the appropriate choice from a drop-down list. If they select the option to continue the conversation further, the agent delivers the next piece of conversation, which includes more detailed feedback on the issue at hand; else, the agent stops the conversation and lets the student continue working, while monitoring their strategic and affective processes in the background (for providing future scaffolds). This process allows the learner to have some decision-making agency with respect to the
(a) FigureIV.4:ConversationtreesfordifferentRead→Buildstrategicscaffolds
(b)
Figure IV.4: Conversation trees for different Quiz→Build strategic scaffolds
(c)
Figure IV.4: Conversation trees for different Quiz→Read strategic scaffolds
(d)
Figure IV.4: Conversation trees for affective-cognitive scaffolds
amount of feedback they receive at a time and consider to be sufficient to meed their current needs.
IV.4.2.2 Scaffold Level
Our scaffolding framework includes an additionalscaffold levelfeature to further increase the adaptivity of student support through the conversation trees.
1. Learners are initially offered aLevel-0feedback, especially the first time they receive a scaffold related to a particular ineffective strategy, e.g., an ineffective Read→Build pattern. The Level-0 feedback offers general hints to help the learners become more strategic in their work. For example, in the earlier Read→Build scenario, depending on the specific Build issue, the learner would be told how they can read their resource pages more strategically to extract the correct causal links and fix their errors.
2. If the same strategy pattern is observed again during the student’s learning process, suggesting that they have been unable to develop an effective strategy to counter their errors, the next time the student would be scaffolded with aLevel-1feedback. The Level-1 feedback includes more specific hints, contextual- ized to the student’s current learning difficulty, to help them better understand and apply the suggested feedback. In the ineffective Read→Build scenario, this may be informing the student about concepts related to erroneous causal links on their map and then helping the student locate specific pages in their science book (or specific sections within pages) that they can review to fix the errors.
TableB.2illustrates the concept of ’scaffold level’ with examples from a prior version of adaptive scaf- folding (Munshi et al.,2022a), where we can observe how the scaffold level determines the level of contex- tualization of conversational feedback for the same type of triggering conditions. The inclusion of scaffold levels in the scaffolding framework helps to further tailor the level of support to learners’ needs, by allowing them to develop their strategic processes more independently in initial stages and providing more corrective hints if they are unable to develop and apply effective strategies even after the initial support. However, even in the higher-level (more contextualized scaffolds), our design of conversations ensures that the learner only receives supportive hints and not bottom-out hints e.g., by being told the exact causal links they should build to get a particular quiz answer graded as correct. This is done to discourage ineffective behaviors like gaming the system (Baker et al.,2004,2008).
IV.4.3 Findings from Design-Based Research Studies that Informed the Current Scaffold Design