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Ionic liquids (ILs) exhibit unique properties that have led to their development and widespread use for a variety of applications. Development efforts have generally focused on achieving desired macroscopic properties via tuning of the IL through variation of the cations and anions. Both the macroscopic and microscopic properties of an IL influence its tunability and thus feasibility of use for selected applications. Works geared toward a microscopic understanding of the nature and strength of the intrinsic cation-anion interactions of ILs have been limited to date. Specifically, the intrinsic strength of the cation-anion interactions in ILs is largely unknown. In previous work, we employed threshold collision-induced dissociation (TCID) approaches supported and enhanced by electronic structure calculations to determine the bond dissociation energies (BDEs) and characterize the nature of the cation-anion interactions in a series of four 2:1 clusters of 1-alkyl-3-methylimidazolium cations with the hexafluorophosphate anion, [2C n mim:PF 6 ] + . To examine the effects of the 1-alkyl chain on the structure and energetics of binding, the cation was varied over the series: 1-ethyl-3-methylimidazolium, [C 2 mim] + , 1-butyl-3-methylimidazolium, [C 4 mim] + , 1-hexyl-3-methylimidazolium, [C 6 mim] + , and 1-octyl-3-methylimidazolium, [C 8 mim] + . The variation in the strength of binding among these [2C n mim:PF 6 ] + clusters was found to be similar in magnitude to the average experimental uncertainty in the measurements. To definitively establish an absolute order of binding among these [2C n mim:PF 6 ] + clusters, we extend this work again using TCID and electronic structure theory approaches to include competitive binding studies of three mixed 2:1 clusters of 1-alkyl-3-methylimidazolium cations and the hexafluorophosphate anion, [C n-2 mim:PF 6 :C n mim] + for n = 4, 6, and 8. The absolute BDEs of these mixed [C n-2 mim:PF 6 :C n mim] + clusters as well as the absolute difference in the strength of the intrinsic binding interactions as a function of the cation are determined with significantly improved precision. By combining the thermochemical results of the previous independent and present competitive measurements, the BDEs of the [2C n mim:PF 6 ] + clusters are both more accurately and more precisely determined. Comparisons are made to results for the analogous [2C n mim:BF 4 ] + and [C n-2 mim:PF 6 :C n mim] + clusters previously examined to elucidate the effects of the [PF 6 ] - and [BF 4 ] - anions on the binding. 

Measurement informs our actions and decisions well beyond school, necessitating that students develop a conceptual understanding of measurement alongside the procedural ability to measure objects. We present a first attempt to explore how students express their understanding of measurement by analyzing the behavior of college and elementary students as they completed measurement estimation tasks. We clustered observable student behavior to identify six profiles of behavioral strategies which may indicate different levels of conceptual understanding. 

Measurement informs our actions and decisions well beyond school, necessitating that students develop a conceptual understanding of measurement alongside the procedural ability to measure objects. We present a first attempt to explore how students express their understanding of measurement by analyzing the behavior of college and elementary students as they completed measurement estimation tasks. We clustered observable student behavior to identify six profiles of behavioral strategies which may indicate different levels of conceptual understanding. 

Measurement informs our actions and decisions well beyond school, necessitating that students develop a conceptual understanding of measurement alongside the procedural ability to measure objects. We present a first attempt to explore how students express their understanding of measurement by analyzing the behavior of college and elementary students as they completed measurement estimation tasks. We clustered observable student behavior to identify six profiles of behavioral strategies which may indicate different levels of conceptual understanding. 

A significant amount of research has illustrated the impact of student emotional and affective state on learning outcomes. Just as human teachers and tutors often adapt instruction to accommodate changes in student affect, the ability for computer-based systems to similarly become affect-aware, detecting and personalizing instruction in response to student affective state, could significantly improve student learning. Personalized and affective interventions in tutoring systems can be realized through affect-aware learning technologies to deter students from practicing poor learning behaviors in response to negative affective states and to optimize the amount of learning that occurs over time. In this paper, we build off previous work in affect detection within intelligent tutoring systems (ITS) by applying two methodologies to develop sensor-free models of student affect with only data recorded from middle-school students interacting with an ITS. We develop models of four affective states to evaluate and determine significant predictors of affect. Namely, we develop a model which discerns students’ reported interest significantly better than majority class. 

A substantial amount of research has been conducted by the educational data mining community to track and model learning. Previous work in modeling student knowledge has focused on predicting student performance at the problem level. While informative, problem-to-problem predictions leave little time for interventions within the system and relatively no time for human interventions. As such, modeling student performance at higher levels, such as by assignment, may provide a better opportunity to develop and apply learning interventions preemptively to remedy gaps in student knowledge. We aim to identify assignment-level features that predict whether or a not a student will finish their next homework assignment once started. We employ logistic regression models to test which features best predict whether a student will be a “starter” or a “finisher” on the next assignment.